Title :
Categorical data analysis using multiobjective differential evolution based fuzzy clustering
Author :
Saha, Indranil ; Maity, Debasree ; Maulik, Ujjwal
Author_Institution :
Dept. of Comput. Sci. & Eng., Jadavpur Univ., Kolkata, India
Abstract :
During the last one decade, rapid proliferation of categorical data attracts computer scientists and engineers to analyse the categorical data. In this regard, unsupervised technique, such as clustering has been used by inception of new algorithms or modification of the existing ones. These methods are basically optimizing single objective function to get the partitions. However, optimization of multiple conflicting objectives simultaneously may evolve better clustering results as the multiobjective optimization techniques have been successfully applied in various fields of engineering and science. Hence, in this article, Multiobjective Differential Evolution based Fuzzy Clustering for Categorical Data is proposed. For this purpose, differential evolution is used as an underlying optimization technique. Moreover, the index encoding scheme is used to encode the vector in differential evolution and after the mutation operation in differential evolution, scaling is introduced to adjust the encoded index value within the permissible range of categorical objects. The performance of the proposed method has been demonstrated by comparing it with the widely used state-of-the-art methods for two synthetic and two real life data sets. Finally, statistical test has been conducted to judge the superiority of the proposed method.
Keywords :
Pareto optimisation; data analysis; encoding; evolutionary computation; fuzzy set theory; pattern clustering; statistical testing; Pareto optimisation; categorical data analysis; categorical data proliferation; index encoding scheme; multiobjective differential evolution-based fuzzy clustering; multiobjective optimization techniques; multiple-conflicting objective optimization; mutation operation; permissible categorical object range; real life data sets; scaling; statistical test; synthetic data sets; unsupervised technique; vector encoding; Clustering algorithms; Indexes; Optimization; Partitioning algorithms; Sociology; Statistics; Vectors; Categorical attributes; differential evolution; fuzzy clustering; multiobjective; pareto optimality;
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on
Conference_Location :
Mysore
Print_ISBN :
978-1-4799-2432-5
DOI :
10.1109/ICACCI.2013.6637491