文章基本信息

标题：Entropy Reduction Based On K-Means Clustering And Neural Network/SVM Classifier
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作者：Parampreet Kaur ; Mr. Sahil Vashist ; Roopkamal Ahluwalia 等
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2014
卷号：3
期号：11
页码：9166-9168
出版社：IJECS
摘要：Clustering is the unsupervised learning problem. Better Clustering improves accuracy of search results and helps to reducethe retrieval time. Clustering dispersion known as entropy which is the disorderness that occur after retrieving search result. It can bereduced by combining clustering algorithm with the classifier. Clustering with weighted k-mean results in unlabelled data. This paperpresent a clustering algorithm called Minkowski Weighted K-Means. This algorithm automatically calculates feature weights for eachcluster and uses the Minkowski metric (Lp) Unlabelled data can be labeled by using neural network and support vector machines. A neuralnetwork is an interconnected group of nodes, for classifying data whereas SVM is the classification function to distinguish between membersof the two classes in the training data. For classification we use neural networks and SVM as they can recognize the patterns. The wholework is taken place in the Matlab.7 environment
关键词：Clustering; K Means; SVM; Neural Network; Entropy