The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
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
ACM Computing Surveys (CSUR)
Vector quantization based on genetic simulated annealing
Signal Processing
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
On the performance of ant-based clustering
Design and application of hybrid intelligent systems
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Adaptation of the F-measure to cluster based lexicon quality evaluation
Evalinitiatives '03 Proceedings of the EACL 2003 Workshop on Evaluation Initiatives in Natural Language Processing: are evaluation methods, metrics and resources reusable?
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
Data clustering based on an efficient hybrid of K-harmonic means, PSO and GA
Transactions on computational collective intelligence IV
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
A new grouping genetic algorithm for clustering problems
Expert Systems with Applications: An International Journal
Cognitive intentionality extraction from discourse with pragmatic-tree construction and analysis
Information Sciences: an International Journal
Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
International Journal of Information Retrieval Research
A novel ant-based clustering algorithm using Renyi entropy
Applied Soft Computing
| Hi-index | 12.06 |
Clustering is an unsupervised learning procedure and there is no a prior knowledge of data distribution. It organizes a set of objects/data into similar groups called clusters, and the objects within one cluster are highly similar and dissimilar with the objects in other clusters. The classic K-means algorithm (KM) is the most popular clustering algorithm for its easy implementation and fast working. But KM is very sensitive to initialization, the better centers we choose, the better results we get. Also, it is easily trapped in local optimal. The K-harmonic means algorithm (KHM) is less sensitive to the initialization than the KM algorithm. The Ant clustering algorithm (ACA) can avoid trapping in local optimal solution. In this paper, we will propose a new clustering algorithm using the Ant clustering algorithm with K-harmonic means clustering (ACAKHM). The experiment results on three well-known data sets like Iris and two other artificial data sets indicate the superiority of the ACAKHM algorithm. At last the performance of the ACAKHM algorithm is compared with the ACA and the KHM algorithm.