Algorithms for clustering data
Algorithms for clustering data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Cluster Formation Using Level Set Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper proposes an adaptive subcluster merging (ASM) based clustering algorithm. The ASM algorithm has two stages: subcluster partition and subcluster merging. Specifically, it first applies local expanding with variance constraint to partition subclusters with uniform granularity, and then it adaptively merges the subclusters into clusters with the notion of density. Through these two stages, ASM algorithm can identify clusters of heterogeneous structures. The feasibility of the algorithm has been successfully tested on both synthetic and real-world data sets. Comparative experimental studies of various clustering algorithms are also performed. The results demonstrate that the proposed algorithm performs better than K-means, complete-link hierarchial, density-based and maximum variance algorithms.