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an agglomerative hierachical clustering algorithm is developed and the Merge Dissimilarity Indexes
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)
data clustering
Keke Chen, Hua Yan, Ling Liu. SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2009 ;.  (349.91 KB)
Framework
Hua Yan, Keke Chen, Ling Liu, Zhang Yi. SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2010 ;.  (349.91 KB)
Large Data Clusters
Hua Yan, Keke Chen, Ling Liu, Zhang Yi. SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2010 ;.  (349.91 KB)
The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)
we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)
we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity measure. Based on the above measure
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)