Multidimensional data clustering utilizing hybrid search strategies
Pattern Recognition
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Elements of information theory
Elements of information theory
Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
ACM Computing Surveys (CSUR)
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
A vector space model for automatic indexing
Communications of the ACM
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
A parallel hybrid web document clustering algorithm and its performance study
The Journal of Supercomputing - Special issue: Parallel and distributed processing and applications
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Pattern Recognition
An improved sequential clustering algorithm
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Cooperative clustering for software modularization
Journal of Systems and Software
CRUDAW: a novel fuzzy technique for clustering records following user defined attribute weights
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Hi-index | 0.01 |
Bisecting k-means (BKM) is very attractive in many applications as document-retrieval/indexing and gene expression analysis problems. However, in some scenarios when a fraction of the dataset is left behind with no other way to re-cluster it again at each level of the binary tree, a ''refinement'' is needed to re-cluster the resulting solutions. Current approaches to refine the clustering solutions produced by the BKM employ end-result enhancement using k-means (KM) clustering. In this hybrid model, KM waits for the former BKM to finish its clustering and then it takes the final set of centroids as initial seeds for a better refinement. In this paper, a cooperative bisecting k-means (CBKM) clustering algorithm is presented. The CBKM concurrently combines the results of the BKM and KM at each level of the binary hierarchical tree using cooperative and merging matrices. Undertaken experimental results show that the CBKM achieves better clustering quality than that of KM, BKM, and single linkage (SL) algorithms with comparable time performance over a number of artificial, text documents, and gene expression datasets.