alexa Hybrid Clustering and Classification for Entropy Reduc
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
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Research Article

Hybrid Clustering and Classification for Entropy Reduction: A Review

Palwinder kaur1,Usvir kaur2 ,Dr.Dheerendra Singh3
  1. Student, Department of computer science and engineering, S.G.G.S.W. University, fatehgarh sahib, Punjab, India.
  2. Assistant professor, Department of computer science and engineering, S.G.G.S.W.University, fatehgarh sahib, Punjab. India.
  3. Professor, Department of computer science and engineering, Shaheed udham singh college of engineering and technology, Tangori, Mohali, Punjab, India.
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Abstract

Clustering is the unsupervised learning problem. Better Clustering improves accuracy of search results and helps to reduce the retrieval time. Clustering dispersion known as entropy which is the disorderness that occur after retrieving search result. It can be reduced by combining clustering algorithm with the classifier. Clustering with weighted k-mean results in unlabelled data. Unlabelled data can be labelled by using neural network and support vector machines. A neural network is an interconnected group of nodes, for classifying data whereas SVM is the classification function to distinguish between members of the two classes in the training data. For classification we use neural networks and SVM as they can recognize the patterns.

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