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  • 标题:Business Analysis and Decision Making Through Unsupervised Classification of Mixed Data Type of Attributes through Genetic Algorithm
  • 本地全文:下载
  • 作者:Rohit Rastogi ; Saumya Agarwal ; Palak Sharma
  • 期刊名称:BVICAM's International Journal of Information Technology
  • 印刷版ISSN:0973-5658
  • 出版年度:2014
  • 卷号:6
  • 期号:1
  • 语种:English
  • 出版社:Bharati Vidyapeeth's Institute of Computer Applications and Management
  • 摘要:Grouping or unsupervised classification has variety of demands in which the major one is the capability of the chosen clustering approach to deal with scalability and to handle the mixed variety of data set. There are variety of data sets like categorical/nominal, ordinal, binary (symmetric or asymmetric), ratio and interval scaled variables. In the present scenario, latest approaches of unsupervised classification are Swarm Optimization based, Customer Segmentation based, Soft Computing methods like Fuzzy Based and GA based, Entropy Based methods and hierarchical approaches. These approaches have two serious bottlenecks…Either they are hybrid mathematical techniques or large computation demanding which increases their complexity and hence compromises with accuracy. It is very easy to compare and analyze that unsupervised classification by Genetic Algorithm is feasible, suitable and efficient for high-dimensional data sets with mixed data values that are obtained from real life results, events and happenings.
  • 关键词:Index Terms – Clustering;Clustering Algorithms; Categorical Dataset;Numerical Dataset;Data Mining; Pattern Discovery;Genetic Algorithm.
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