[01] Sergio Komini, School of Computer Science, Universidad de Chile, Santiago, Chile. [02] Margaret Siredon, School of Computer Science, Universidad de Chile, Santiago, Chile.

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. A clustering ensmble framework based on the new metric is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword and is also frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In this paper we proposed a combined model of HMM and C-Mediod to Item dataset clustering.

Clustering Ensemble, Stability Measure, Extended EAC, Hidden Markov Model, Sequences, C-Mediod, Data Mining.

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