Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Autonomous knowledge-oriented clustering using decision-theoretic rough set theory
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Automatically determining the number of clusters using decision-theoretic rough set
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Autonomous Knowledge-oriented Clustering Using Decision-Theoretic Rough Set Theory
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
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This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was established by applying the hierarchical agglomerative clustering algorithm (HACA) (Han and Kamber, 2001) before the feature set changed. We aim to reach the result more efficiently than running HACA again from scratch on the feature-extended object set. Experiments testing the method's efficiency and a practical distributed systems problem in which the HAC method can be efficiently used (the problem of adaptive horizontal fragmentation in object oriented databases) are also reported.