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Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems. Presenter : Chuang, Kai-Ting Authors : Yan Yang, Wei Tan, Tianrui Li, Da Ruan 2012, KBS. Outlines. Motivation Objectives Methodology
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Consensus clustering based on constrained self-organizing map and improved Cop-Kmeans ensemble in intelligent decision support systems Presenter : Chuang, Kai-TingAuthors : Yan Yang, Wei Tan, Tianrui Li, Da Ruan2012, KBS
Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments
Motivation • Cop-Kmeans is a K-means variant that incorporates background knowledge in the form of pairwise constraints. • However, there exists a constraint violation in Cop-Kmeans.
Objectives • An improved Cop-Kmeans (ICop-Kmeans) algorithm to solve the constraint violation of Cop-Kmeans. • A new constrained self-organizing map (SOM) to enhancing the performance of ICop-Kmeans.
Methodology-COP-SOM Competition layer Input layer
Conclusions • The proposed methods could effectively overcome disadvantages of Cop-Kmeans, and Icop-Kmeans performs better using the produced order and its performance is further enhanced using the clustering ensemble technique.
Comments • Advantages • The approach is helpful. • Applications • SOM.