Ant clustering algorithm different from previous ant-based clustering in a number of key areas. First, they are particularly suit- Oct 1, 2019 · The proposed algorithm can help to increase search speed by optimizing the process. ACK [49], which is ant-based clustering algorithm integrated with kernel method and proposed by us. Mar 1, 2024 · In this article, we propose a scalable hybrid quantum classical algorithm that combines the ant colony optimization algorithm with the K-means clustering method. (3) Run the k-means algorithm to cluster data into N partitions based on the value of Den(i). A stream is In this video, we'll look at clustering, specicially the Lumer-Faieta algorithm. Inspired by nature, Ant Colony based Clustering arises from ant colony behavior in organizing nests and clustering ants corpses. In this paper May 1, 2013 · Ant-based clustering is a type of clustering algorithm that imitates the behavior of ants. Readme Activity. Xu and B. But it has some shortcomings such as its sensitivity to initial condition, and it is easy to fall in local peak. This algorithm has been implemented and tested on several simulated and real datasets. , 2021; Zanetti et al. Ant-based clustering stands out as the most widely used group of swarm-based clustering algorithms. ACSC identifies clusters as groups of micro-clusters. This approach can partially overcome the hardware limitations of currently available real quantum computers and is capable of solving problems of a substantial scale, with potential Nov 13, 2007 · Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as hierarchical clustering and k-means. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. May 13, 2021 · Biologically inspired computing techniques are very effective and useful in many areas of research including data clustering. Jan 1, 2008 · Ant clustering algorithm is a nature-inspired clustering technique which is extensively studied for over two decades. As a swarm-intelligent method, the ant colony clustering algorithm (ACCA) is inspired by the behavior of ant colonies. 99% Nov 18, 2010 · Experimental results on clustering benchmarks indicate that the proposed algorithm has better performance than LF, and overcomes LF's shortcomings of lower convergence speed and longer iteration cycles. Many conventional methods for concepts formation in ontology learning have relied on the use of predefined templates and rules, and static An image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA), which models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path, and is applied to detection of microcalcifications in digital mammograms. Ant colony clustering has the characteristics of automatically clustering, high clustering accuracy, irregular cluster shapes and so on. Apr 1, 2017 · The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. All these attempts prove that MapReduce Oct 29, 2007 · An ant colony clustering algorithm for optimally clustering N objects into K clusters that employs the global pheromone updating and the heuristic information to construct clustering solutions and uniform crossover operator to further improve solutions discovered by ants. Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant Dec 1, 2010 · The standard Ant clustering algorithm (ACA) was proposed by Lumer and Faieta (1994), and it closely mimics the ant behavior described in Ant-based clustering written by Deneubourg et al. The Hybrid Ant Clustering Algorithm (hACA) The hybrid ant clustering algorithm (hACA) is described in Algorithm 1. Ant clustering algorithm can deal with multi-dimensional and non-uniform database. This paper shows how the Ant Clustering Algorithm (ACA) is implemented in clustering and classifying the tropical wood species. Jul 4, 2022 · Since the ant colony clustering algorithm is more accurate, but the convergence speed is slow, and the K-means algorithm has a faster convergence speed than the ant colony algorithm, but the accuracy is relatively low, and it is easy to form a local optimal result, so the advantages of the two algorithms are combined After that, the purpose of A Dyeing Clustering Algorithm based on Ant Colony Path-finding Resources. Ants perform random search for food. Based on the characteristics of ant-based clustering algorithm, the paper implements the parallelization of utilizes an Ant System based Clustering Algorithm (ASCA) for image segmentation as it achieved more significant results as summarized in the following text. Modified 11 years, 8 months ago. As we described in Section II, the movement of ants is determined by a genetic algorithm rather than random walk to one of the neighbors. Further, we will describe how to define a similarity function for images and how the implementation is used to cluster images of cars from the Feb 22, 2024 · The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing Thereby, there are numerous clustering algorithms: hierarchical and partitional. Ant-based clustering can be divided into two classes. In the algorithm, each Ant Clustering Algorithms is a swarm intelligent method on clustering objects by their similarity to each other, and it was inspired by ant colonies found in nature. The current cell of an ant in the grid world is represented by a location which includes two In this section, many existing ant colony-based clustering algorithms and clustering solution evaluation using cluster validity indices are presented. The lifetime of clusters and number of CHs determines the efficiency of network. The tumbling window model is used to read a stream and rough Jun 1, 2023 · The self-adjusting ant colony clustering algorithm based on a correction mechanism3. The clustering algorithm based on object function resolves the clustering problem into optimization problem, thereby it becomes to the main investigatory stream nowadays. Step 1: Improved Ant Colony Clustering Algorithm: 2. Ant colony optimization was introduced by Marco Dorigo [8]. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency. At first, according to the tasks’ preference for resources, fuzzy clustering algorithm is used to divide the resources in order to reduce the space May 25, 2020 · Add a description, image, and links to the ant-clustering-algorithm topic page so that developers can more easily learn about it. Clustering analysis is one kind of pattern recognition that not to be supervised. Oct 8, 2024 · In this work, we introduces a quantum-classical hybrid ant colony optimization algorithm (QACO) combined with the K-means clustering method. In this study, we extend the ant clustering algorithm (ACA) to a Dec 29, 2015 · The ant colony clustering algorithm is a swarm-intelligent method for solving clustering problems that is inspired by the behavior of ant colonies in clustering their corpses and sorting their larvae. Clustering has been a widely studied problem in a variety of application domains including data mining, knowledge discovery, artificial intelligence and etc. In this paper, we propose an ant colony clustering algorithm based on swarm intelligence. IntheASMmodel,eachdatumwasrepresented Keywords: Ant clustering algorithm, data clustering, visual data mining Received: July 15, 2004 Among the many bio-inspired techniques, ant-based clustering algorithms have received special atten-tion from the community over the past few years for two main reasons. Two measurements and five data sets are used in experiments. To attack the slow speed of the AntClass algorithm, a new algorithm named DBAntCluster is This repository is a code experiment for visualizing the Ant Colony Optimization (ACO) clustering algorithm. Hadoop is a distributed system infrastructure of cloud computing. The first algorithm is used to solve the classical shortest path problem and the second algorithm is used to perform dynamic clustering on large data sets. The agents' environment is a two-dimensional grid. How to use the data mining and analysis algorithm to carry out e-commerce recommendation has become one of the important contents of the current information technology development. The results of hybrid algorithm are better than DBSCAN and PDBSCAN algorithms. 1 Ant colony clustering process 1) Calculate distance matrix Each structure plane is seen as an object with two properties, tendency and inclination. Industrial applications of computer vision sometimes require Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. Nov 17, 2023 · This paper explores the research of task planning and platform–payload metric matching in rapid cluster search. Specifically, we include a genetic algorithm in Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. The ACA-ITL algorithm always achieves results that are slightly better than the results of ACA clustering algorithm (for the presented evaluation metrics). At the core of the algorithm we use both the accumulated Jan 1, 2004 · Inspired by this, we propose an algorithm, FAC2T(Fusion using Ant Colony Clustering Technique), to obtain an estimate of the globally optimal clustering solution by fusing the solutions obtained Jun 17, 2012 · Results on the Hadoop clusters show that the parallelization of ant-based clustering algorithm using MapReduce can significantly improve the computational efficiency with the premise of maintaining clustering accuracy. ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. In: IEEE International Conference on Systems Man and Cybernetics Systems, SMC 2009, San Antonio, TX, October 11-14, pp. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. Red ants - carry food, blue ants - do no carry food, orange - foodThis is a Python implementation of an algorithm propu. Broadly speaking, there are two main types of ant-based clustering: the first group of methods directly mimics the clustering behavior Feb 1, 2015 · A novel design of IDS is presented by combining two existing bio-inspired machine learning algorithms; Artificial Immune System (AIS) and Ant Clustering Algorithm (ACA), and evaluates the pros and cons of the approach. Apr 18, 2020 · An ant colony optimization approach for partitioning a set of objects is proposed. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Ant clustering algorithm is a nature-inspired clustering technique which is extensively studied for over two decades. Apr 8, 2013 · Ant Clustering Algorithm in Python. Mutual distance matrix Dn×n of structure surface is calculated, where A is the total number of the structural surface to be grouped. The ant colony clustering algorithm is a swarm-intelligent method used for Dec 8, 2023 · We implement AntClust, a clustering algorithm based on the chemical recognition system of ants and use it to cluster images of cars. ACO-C works in a multi-objective setting and yields a set of non-dominated solutions. This paper introduces two improvements that can be incorporated into any ant clustering algorithm: kernel function similarity weights and a similarity memory model replacement scheme. The algorithm Sep 1, 2006 · In this paper, ant colony algorithm was improved from two aspects, then a novel hybrid ant colony and agglomerate document clustering algorithm, hybrid-AC&A, has been proposed based on ant colony Apr 1, 2022 · This section presents the key principles of the proposed Adaptive Ant Colony Optimization with Node Clustering (AACO-NC). Nov 1, 2024 · A genetic clustering algorithm was proposed to cluster objective functions by leveraging the strong search ability of GA (Murthy and Chowdhury, 1996). It is based on the foraging behavior of ants. In most ant-based clustering algorithms, each ant lacks global visibility and only uses local search to find the 3 Ant colony ATTA clustering algorithm 3. However, due to the hardware resource limitations of currently available quantum computers, such as the limited number of qubits, lack of high-fidelity gating operation Jul 11, 2021 · Biologically inspired computing techniques are very effective and useful in many areas of research including data clustering. The algorithm employs the global pheromone updating and the heuristic information to Sep 24, 2021 · In this paper, a novel Gaussian ant colony algorithm, for clustering or spatial overlap cell state and number estimator, simultaneously, is proposed. Apr 4, 2024 · The improved Ant Colony Optimization (ACO) algorithm is applied to reduce inter-cluster transmission cost. ACO-C has two pre-processing steps: neighborhood construction and data set reduction. Randomly determine the starting point of the ant: 6. To solve this problem, this paper proposes an analysis method based on the improved ant colony clustering algorithm, where cosine distance and Euclidean distance were combined to Apr 1, 2024 · Ant Colony Algorithm (ACO) Introduced by Dorigo (Citation 1992), ant colony optimization is an algorithm inspired by the foraging behavior observed in ants. Jun 17, 2012 · Plenty of clustering algorithms based on MapReduce emerge including K-means [26], Canopy [27], Cop-Kmeans [20] and the ant-based clustering algorithm [28]. Jun 1, 2023 · This paper improved the ant colony clustering algorithm by introducing a correction mechanism to correct outlier points to improve model classification accuracy; introducing an increased flow rate ρ to improve the speed and stability of model convergence; and realising that the next transfer target is selected by a true self-adjusting transfer method where the transfer probability is Aug 18, 2005 · Experimental results on several real datasets and benchmarks indicate that the algorithm can find clusters more quickly and with better quality than K-means and LF. Aug 29, 2024 · ECG Arrhythmia classification Based on Improved Ant Colony Clustering Algorithm; 1. Oct 1, 2010 · Jafar and Sivakumar [105], in their paper, gave a brief review on the application of biologically inspired data clustering technique with a focus on the ant-based clustering algorithms. The ant’s state is controlled by a function of the ant’s fitness to the environment it locates and a Sep 15, 2024 · ECG Arrhythmia classification Based on Improved Ant Colony Clustering Algorithm; 1. The literature (Krishna and Murty, 1999) introduced GKA (Genetic K-means Algorithm), a hybrid clustering algorithm that combines GA with K-means to represent a novel direction in genetic k-means clustering research. Partitioning-based DBSCAN reduces the sensitivity of initial parameters of DBSCAN. Mar 1, 2015 · Next, using the two objective functions we present a novel clustering methodology based on Ant Colony Optimization (ACO-C). When moving on a heterogeneous 2-dimensional lattice of vectors, global behavior emerges from the local interaction between the ants Feb 4, 2020 · Ant Colony Optimization (ACO) algorithm is a population based optimization algorithm can be used for varying the centroid and optimizing the cluster formation [1,2,3,4,5,6]. The core idea of this approach is to divide large-scale TSP problems into multiple smaller subgroups using K-means clustering, then employ quantum computers to find optimal solutions for these subgroups, and finally refine the global solution through ing model (ASM) and an adaptive artificial ant clustering algorithm (A4C) to solve the clustering problem in data mining. This algorithm can solve complicated clustering problems very well. In the ACCA, ants Formalize the Ant Clustering Algorithm (ACA) in pseudo-code or in words. while i ≤ number of iterations: 4. We established a cluster search task−planning algorithm framework, combining an improved sequential clustering algorithm, ant colony algorithm, particle swarm optimization algorithm, and crow search optimization algorithm. , 2017), electricity consumption data as well as electricity consumption data features are analyzed to determine whether they are highly correlated with electricity theft. The ACA, Ant Clustering Algorithm, simulates the ants' behavior on the anthill cleaning. (1991). 0 forks Report repository Releases Oct 1, 2024 · This article proposes a novel dyeing clustering algorithm based on ant colony pathfinding, with the following contributions: (1) To the best of our knowledge, this is the first time that the idea of dyeing has been applied to ant colony clustering, which can make the algorithm have higher fault tolerance, so as to avoid large clustering errors if the wrong cluster center is selected. In the ASM mode, each data is represented by an agent. To prevent harm from intruders the individual ants of a colony have developed a mechanism to recognize their nestmates. The Ant Clustering Algorithm (ACA) is a biological inspired data clustering technique, which aimed to cluster and classify the data patterns into different groups. The algorithm, called KANTS, consists on a set of equations that model the Jan 1, 2023 · The scoring analysis method of English composition review lacks flexibility. To differentiate the proposed approach from these previously mentioned, we call our algorithm, AACA (Adaptive Ant-Clustering Algorithm), which is more robust in terms of the number of clusters found and tends to converge into good solutions while the clustering process evolved Dec 1, 2010 · Ant clustering algorithm with K-harmonic means clustering @article{Jiang2010AntCA, title={Ant clustering algorithm with K-harmonic means clustering}, author={Hua Clustering analysis is one of the data analysis techniques that organizes items into clusters according to their degrees of similarities. There are two types of Ant System (AS) based image segmentation algorithms were proposed in the literature. The ant colony clustering algorithm. The main goal is to put similar objects together. Randomly determine the starting point of the ant6. The actual clustering effectiveness of adACA is expected to be enriched. Ant system-based clustering algorithm Jul 1, 2024 · Machine learning-based ETD methods are usually combined with electricity consumption data features. (), who proposed a basic model that allows ants to randomly move, pick up and drop objects according to the number of similar surrounding objects so as to Mar 1, 2016 · The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. Therefore, in this paper, research on ant colony clustering algorithm for personalized recommendation of e-commerce was proposed. Ant-Colony Stream Clustering (ACSC) algorithm is a density based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. The algorithm uses a digraph where the vertices represent the data to be clustered. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents. 1 Ant colony clustering algorithm. 1431–1438 (2009) Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. To overcome these deficiencies Oct 1, 2024 · This article proposes a novel dyeing clustering algorithm based on ant colony pathfinding, with the following contributions: (1) To the best of our knowledge, this is the first time that the idea of dyeing has been applied to ant colony clustering, which can make the algorithm have higher fault tolerance, so as to avoid large clustering errors if the wrong cluster center is selected. while a ≤ number of ants: 5. In this paper, we will propose a new clustering algorithm using the Ant clustering algorithm with K-harmonic means clustering (ACAKHM). The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. ACO is an iterative algorithm, which has multiple calculations for fitness function. This paper presents an ant colony clustering algorithm for optimally clustering N objects into K clusters. An artificial ant sleeping model (ASM) and adaptive artificial ants clustering algorithm (A /sup 4/C) are presented to resolve the clustering problem in data mining by Jun 1, 2016 · The benchmarking experiments show that two ant-based clustering approaches perform very well compared to each other for synthetic data sets (see Table 1). The weight of the edge represents the acceptance rate between In DBAntCluster algorithm, the ants can avoid many unnecessary movements by using the data attribute of density and distribution well, and the speed is greatly accelerated. NAC-RE-K: The algorithm is designed for comparing KPCA and KECA. In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. Data clustering is one of important research topics of data mining. May 17, 2020 · Algorithms such as the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are examples of swarm intelligence and metaheuristics. The cluster centers found by the ants are evaluated using a reformulated Fuzzy C Means Apr 1, 2022 · Automatic clustering algorithms refer to any clustering techniques used to automatically determine the number of clusters without having any prior information of the dataset features and attributes (Ezugwu, 2020a). Early work demonstr … Aug 1, 2011 · We present a partitioning-based DBSCAN algorithm with ant clustering algorithm. We propose an adaptive ant colony data clustering algorithm for a dynamic database. Jul 5, 2017 · Due to in the early period of the ant colony clustering algorithm convergence speed is very slow, this paper proposes a hybrid clustering algorithm based on ant colony clustering and MMK-means algorithm, which uses MMK-means algorithm to process the data, followed by ant colony clustering to finish clustering. ACSC identifies clusters as groups of micro-clusters. As perspectives, it will be Jan 1, 2020 · In order to better solve the problem of gas outburst prediction, based on the in-depth study of ant colony algorithm, the ant colony clustering algorithm is improved, and the population classification and ant sensory perception characteristics are applied to make the ant colony the most likely to find. In this study, we extend the ant clustering algorithm (ACA) to a hybrid ant clustering algorithm (hACA). Sep 20, 2004 · Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented and the A4C algorithm is significantly better than other clustering methods in terms of both speed and quality. To improve the efficiency, increase the adaptability to non-Gaussian datasets and simplify the parameters of the algorithm, a novel ant-based clustering algorithm using Renyi Entropy (NAC-RE) is proposed. while s ≤ number of sample points: 7. Ant-based clustering stands out as the most widely used group of swarm-based clustering algorithms [5]. In the first step of each iteration A method to adaptively update the parameters and the ants' local movement strategies which greatly improve the speed and the quality of clustering and achieves high autonomy, simplicity and efficiency. The idea is that you have a bunch of agents (called ants) that act in simple predefined ways, but when these agents interact withing a large group, then intelligent behavior An adaptive ant-based clustering algorithm with improved environment perception. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as hierarchical clustering and k-means. The algorithm consists of five steps, shown in Figure 1. Abstract—This paper proposes a new clustering algorithm based on ant colony to solve the unsupervised clustering problem. The algorithm, called KANTS, consists on a set of equations that model the local behavior of simple units (ants) that represent data samples. The idea of the Ant-based clustering is gathering the corpses and sorting the larval of ants. Using machine learning methods such as classification and clustering methods (Kong et al. Ask Question Asked 14 years, 1 month ago. 3. The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering Feb 1, 2015 · In this paper, s a dual-ant clustering algorithm for outlier detection, he advantages and disadvantages of the algorithm pro- put forward some measures to improve the clustering it to outlier detection, and achieved good results [6,7]. In ASM, each ant has two states: a sleeping state and an active state. In ASM, each ant has two states: sleeping state and active state. the proposal of an improved clustering algorithm based on ant colony optimization algorithm (ACO-CA), which is specifically designed to address the complexity of malware identification. These insects form colonies and communicate indirectly by laying down pheromones, which serve as trails leading to food sources for other ants. In addition, the algorithm can also replace the cluster heads adaptively to optimize the communication quality. There This paper presents an online, bio-inspired approach to clustering dynamic data streams. A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. Many automatic data clustering algorithms have been proposed in the literature, and several of them are nature-inspired. ATTA [29], which represents the latest modified algorithm of ant-based clustering. 1 watching Forks. The main process of the ant cluster algorithm is the ant conveying process. Dec 24, 2014 · 2. Accordingly, several researchers Dec 1, 2010 · The K-harmonic means algorithm (KHM) is less sensitive to the initialization than the KM algorithm. ∘. Viewed 932 times Graph clustering algorithm. Dec 1, 2010 · The K-harmonic means algorithm (KHM) is less sensitive to the initialization than the KM algorithm. B. Experiments de … Dec 3, 2024 · To further validate the accuracy of the improved ant colony algorithm, we compared the results obtained from MODIS data with the improved ant colony algorithm, iterative algorithm, maximum entropy algorithm, and basic global threshold algorithm for sea ice detection, and the results showed that the accuracy of the proposed algorithm was 4. Thus, tuning parameters of ant based clustering algorithms is still considered as a challenging task [32]. The goal of swarm intelligence is to design intelligent multi-agent systems by taking inspiration from the collective behaviour of social insects such as ants, termites, bees, wasps, and other animal Feb 21, 2014 · The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles, and Silhouette index was introduced to evaluate the clustering effect. In the first stage of the algorithm ants move the cluster centers in feature space. This paper describes an ant-based clustering algorithms and suggests its application as a swarm art conceptual tool. This algorithm is based on the original ACO algorithm, proposed by the authors for solving TSP, Vehicle Routing Problem (VRP), Multi-Depot VRP (MDVRP) and related optimization problems [49], [50], [51]; it has been enhanced Jun 1, 2023 · This paper improved the ant colony clustering algorithm by introducing a correction mechanism to correct outlier points to improve model classification accuracy; introducing an increased flow rate ρ to improve the speed and stability of model convergence; and realising that the next transfer target is selected by a true self-adjusting transfer method where the transfer probability is An artificial ant sleeping model (ASM) and adaptive artificial ants clustering algorithm (A /sup 4/C) are presented to resolve the clustering problem in data mining by simulating the behaviors of gregarious ant colonies. Hierarchical Ant Colony Clustering Algorithm for Functional MRI. In this work, we propose a hierarchical ant-based clustering algorithm, called HAnt, which can be used to determine regions of interest on fMRI. A kernel function weights objects within an ant’s neighborhood according to the object Dec 8, 2023 · The clustering algorithm’s general idea is based on ant’s chemical recognition system. Jan 25, 2016 · In this study, the ant colony clustering algorithm (ACCA), one of the artificial intelligences, was designed to solve the fuzziness of the multi-spectral image for GLD-infected grapevines. As for feature extraction in this research, two feature extractors are selected to extract wood features Aug 26, 2004 · An artificial ant sleeping model (ASM) and adaptive artificial ants clustering algorithm (A /sup 4/C) are presented to resolve the clustering problem in data mining by simulating the behaviors of gregarious ant colonies. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent data object. The method proposes a dynamic weighting approach based on the idea of Backpropagation to update the cluster centers and assign different importance to different samples in clusters to reduce the influence of outlier samples. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. The Jan 1, 2011 · Algorithm 1 Basic Ant-based Clustering INITIALIZATION Randomly scatter n items on a 2D toroidal grid Let G be a population of agents Each agent in G is randomly assigned (or loaded with) an item MAIN LOOP for iteration = 1 to maxIteration do g := an agent randomly selected from G Let the item carried by g be i g performs a random move on the May 10, 2018 · This paper presents an online, bio-inspired approach to clustering dynamic data streams. The first studies in this field were conducted by Deneubourg et al. Ant colony optimization (ACO) is a population-based meta-heuristic that can be used to find approximate solutions to difficult combinatorial optimization problems. There are two aspects to application of Renyi entropy. Jul 5, 2024 · Subsequently, the ant colony algorithm arranges the printing sequence of these clusters based on the cluster centers, followed by computing the shortest path within each cluster. Dec 1, 2010 · The standard Ant clustering algorithm (ACA) was proposed by Lumer and Faieta (1994), and it closely mimics the ant behavior described in Ant-based clustering written by Deneubourg et al. Stars. The AntClass algorithm is a new algorithm applying ant colony clustering algorithm to cluster analysis, and the result is satisfying. The cluster head utilizes a multi-hop approach to transmit aggregated messages to the BS, effectively addressing the issue of unbalanced network energy consumption and consequently enhancing network lifetime. The ant's state is controlled by a function of the ant's This paper analyzes the application of Ant colony approach on clustering, by implementing it on some of the predominant data sets and also to compare the method with the existing method, and discusses the efficiency of the method based on the results. Aim at the clustering result of traditional ant colony clustering algorithm is not accurate and the algorithm operating efficiency lower, many modified algorithm have been proposed. This algorithm not only improved from the method of calculating the similarity measure and enhanced ant memory, and also Algorithm parameters, Data-set dimensionality , etc. Jan 1, 2016 · The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. Clustering Analysis, which is an important method in data May 31, 2023 · Ant clustering algorithms are a robust and flexible tool for clustering data that have produced some promising results. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. This paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. This is the first time the ACO algorithm has been applied to the clustering problem of malicious software. In this context, bio-inspired algorithms have found success in solving clustering problems. The performance of the proposed algorithm is evaluated through several benchmark data sets. These are important for a clustering algorithm, so ant colony clustering is arousing more and more data mining researchers’ concentration. On data analysis, this task is called data segmentation. Specifically, we include a genetic algorithm in Mar 1, 2015 · The clustering algorithms can be classified into partitional, hierarchical, density-based algorithms, and metaheuristics (simulated annealing, tabu search, evolutionary algorithms, particle swarm optimization, ACO, and so on). Jan 1, 2012 · DENNEUBOURG presents the first ant-based clustering algorithm in 1991. The first class imitates the ant’s foraging behaviour, which involves finding the shortest route between a food source and the nest. m analysis clustering algorithm is a kind of bionic algorithm warm intelligence [8]. In this paper, we propose a new clustering algorithm based on ant colony optimization, called Ant Colony Optimization for Clustering (ACOC). The geometry of the GPS satellite recipient (s), which reflects the recipient (s) of the satellites, has a A comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA), and genetic clustering algorithms (GCA), shows thatACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of Dec 26, 2019 · 2. Oct 15, 2011 · Recently, ant-based clustering, which is a type of clustering algorithm that imitates the behaviour of ants, has earned researchers’ attention. The ant colony clustering algorithm is inspired by the behavior of ant colonies in clustering their corpses and sorting their larvae. In order to minimize the intra-variance, or within sum-of-squares, of the partitioned classes, we construct ant-like solutions by a constructive approach that selects objects to be put The modified ant-based clustering approach developes the classical ant clustering algorithm such as LF method and the ACA, which can operate on different category data sets more swiftly, precisely and efficiently, and maintain the good scalability simultaneously. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. A In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). 1. Jul 1, 2024 · This paper proposes a method of ETD based on self-decision ant colony clustering algorithm. This paper proposes a new ant-based clustering algorithm, Tree-Traversing Ant (TTA), for concepts formation as part of an ontology learning system, and attempts to achieve an adaptable clustering method that is highly scalable and portable across domains. The algorithm uses ant colony optimization principles to find good partitions of the data. LF Algorithm [21] ∘. The first one is based on ants’ foraging Sep 19, 2007 · This paper presents an ant colony clustering algorithm for optimally clustering N objects into K clusters. Two popular ant algorithms are inspired by the two examples mentioned previously. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous Mar 1, 2024 · Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithms and overcomes some limitations of the traditional ACO algorithm. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. ACCA is a heuristic search algorithm for global optimisation with advantages such as discreteness, parallelism, robustness and positive feedback. Oct 27, 2023 · A novel clustering algorithm based on improved ant colony optimization is presented, which integrates various variables to elect cluster heads and adopts a low-latency queuing strategy. To form a cohesive motion trajectory, the nearest nodes between adjacent clusters are linked, culminating in a globally optimal solution. The constructing method, the colony similarity, and the behavior Oct 1, 2021 · The geometry of satellite clustering for the selection of suitable satellite navigation subsets is provided, based on the GDOP (Geometric Precision Dilution) satellite factor cluster with the Ant Colony Optimization (ACO) algorithm, which is more efficient at achieving its optimum value. and an ant algorithm for cluster analysis (A4C) are presented. Apr 1, 2021 · In order to solve the problems of traditional k-means clustering algorithm and ant colony algorithm, in the ant colony algorithm, the center of gravity method is introduced to obtain the initial Jul 24, 2018 · Abstract Ant-based clustering algorithms are inspired by the behaviour of real ants. May 5, 2016 · Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. Similarly, when ant conveys the object to the destination, it also considers the similitude degree between the current object and the surrounding objects to decide whether to Nov 1, 2012 · This paper describes an ant-based clustering algorithms and suggests its application as a swarm art conceptual tool. Existing ECG arrhythmia classification methods based on the ant colony clustering algorithm usually use the Euclidean distance to measure the dissimilarity between different objects. Partitioning method based on modified ant clustering algorithm To deal with high-dimensional data, we give another partitioning method which combine modified ant clustering algorithm and PD-based partitioning method (PACA). Jun 1, 2019 · Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms, it also requires fewer parameters and less computational time. 0 May 3, 2004 · The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The Ant clustering algorithm (ACA) can avoid trapping in local optimal solution. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of May 3, 2004 · This section describes the ant algorithm to solve a clustering problem where the aim is to obtain optimal assignment of N objects in R n to one of the K clusters such that the sum of squared Euclidean distances between each object and the center of the belonging cluster is minimized. 2 stars Watchers. Apr 25, 2014 · The video was recorded with CamStudio. The algorithm employs the global pheromone updating and the heuristic information to construct clustering solutions and uniform crossover operator to further improve solutions discovered by ants. Enlightened by the behaviors of gregarious ant colonies, an artificial ant movement (AM) model and an adaptive ant clustering (AAC) algorithm for this model are presented. This is only a warm-up exercise and is not asking you to solve any particular problem using ACA. ). In A /sup 4/C, the agents can be formed into high-quality clusters by Jan 1, 2006 · Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. Give detailed instructions for calculating the 'perceived fraction of items' f and the interactions of each ant with its environment (pickup, move, drop-off of material). Ants form colonies where many individuals help the colony to survive by maintaining it. We will give a short recap summary of the main working principles of the algorithm as devised by the original paper [1]. Aug 22, 2007 · This paper presents an ant colony clustering algorithm for optimally clustering N objects into K clusters. May 1, 2013 · The ant-based clustering algorithms. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph. We have introduced a novel definition of the Gaussian ant system borrowed from the concept of the multi-Bernoulli random finite set (RFS) in the way that it encourages ants searching for cell Jun 8, 2019 · In this paper, by combining the ACO algorithm and the fuzzy clustering algorithm, we have proposed an ant colony optimization fuzzy clustering algorithm to deal with a NP-hard scheduling problem. 5 days ago · This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). Ant decides whether pick-up the current object by object’s probability conversion function. Based on the basic model of ant colony clustering algorithm, LF, an improved ant colony clustering algorithm (IACC) is proposed. Fitting 5 cluster centers through the LSTM model: 3. The domestic and foreign research on e-commerce recommendation was simply summarized Apr 15, 2013 · Ant colony optimization algorithms represent an interesting subset of nature-inspired algorithms. While searching Abstract—We present a swarm intelligence based algorithm for data clustering. bet qzf hcnh vkktseogx pght vcg pgbnyz oludq sksmg qsiwi