Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On Improving Clustering in Numerical Databases with Artificial Ants
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Improved Ant-Based Clustering and Sorting
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Unsupervised Image Segmentation Using a Colony of Cooperating Ants
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
On the performance of ant-based clustering
Design and application of hybrid intelligent systems
On ACO-Based Fuzzy Clustering for Image Segmentation
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Numerical methods for fuzzy clustering
Information Sciences: an International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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An ant-based approach to heuristic centroid searching is introduced. The proposed algorithm consists of three major stages: path construction, evaluation and pheromone updating. In the first stage, data pieces are deemed ants which probabilistically choose cluster centroids according to the heuristic and pheromone information of clusters. In the second stage, cluster centers are updated and cluster validity is evaluated using Bezdek's partition coefficient. In the third stage, pheromone concentration on clusters is updated. When an ant goes to a cluster, it leaves on this centroid pheromone information, the amount of which is determined by evaluation result obtained in the second stage. Initial cluster number is intentionally chosen to be large and cluster merging is performed once the following two conditions are satisfied: 1. Size of the smallest cluster is smaller than a threshold size proportional to the average cluster size; 2. Distance between the smallest cluster and its nearest one is less than a threshold distance. This merging mechanism is shown to enable auto determination of cluster number. The above stages are iteratively performed for a certain number of iterations. The found centroids are used to initialize FCM clustering algorithm. Results on test data sets show that the partitions found using the ant initialization are better optimized than those obtained from random initializations.