Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A New Symmetry Based Cluster Validity Index: Application to Satellite Image Segmentation
ICIT '06 Proceedings of the 9th International Conference on Information Technology
WAIMW '06 Proceedings of the Seventh International Conference on Web-Age Information Management Workshops
A genetic fuzzy k-Modes algorithm for clustering categorical data
Expert Systems with Applications: An International Journal
Bearing Fault Diagnosis Based on Feature Weighted FCM Cluster Analysis
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 05
Application of Fuzzy Cluster in the Analysis of Prosperity of Power Consumption Market
ICRMEM '08 Proceedings of the 2008 International Conference on Risk Management & Engineering Management
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The study of the auto color image segmentation
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In this study, a novel dynamic agglomerative hierarchical clustering algorithm which combines Boltzmann theory of thermodynamics and a graph-theoretic representation of data objects is put forward for data with non-sphere shape clusters. The new algorithm employs neighbors searching operator and vertices spanning operator to construct the linkage paths between vertices. Additionally, in order to obtain the ideal clusters the temperature coefficient is used to completely adjust the linkage paths between vertices. Experimental results on nine benchmark synthetic datasets with different manifold structure demonstrate the effectiveness of the algorithm as a clustering technique. Compared with the K-means algorithm, a genetic algorithm-based clustering algorithm (GAC) and minimum spanning tree clustering algorithm (MST) for clustering task, the presented algorithm has the ability to identify the number and location of the clusters jointly and its clustering performance is clearly better than that of the aforementioned algorithms for complex manifold structures dataset.