An effective evaluation measure for clustering on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
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Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Neighborhood-Based smoothing of external cluster validity measures
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
ESC: An efficient synchronization-based clustering algorithm
Knowledge-Based Systems
GPU accelerated genetic clustering
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Ranking and selection of unsupervised learning marketing segmentation
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Validating synthetic health datasets for longitudinal clustering
HIKM '13 Proceedings of the Sixth Australasian Workshop on Health Informatics and Knowledge Management - Volume 142
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Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a detailed study of 11 widely used internal clustering validation measures for crisp clustering. From five conventional aspects of clustering, we investigate their validation properties. Experiment results show that S\_Dbw is the only internal validation measure which performs well in all five aspects, while other measures have certain limitations in different application scenarios.