Clustering Techniques with Dimensionality Reduction for Data Mining

Dr. J. Thirumaran¹

¹Dean, Computer Science, Rathinam College of Arts and Science, Coimbatore

Abstract

Mining Data from large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, Content Management are regularly confront the problem of dimensionality reduction. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs of 30,000 neurons. Here we describe an approach Bayesian Personalize to solve dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as Filter Algorithm or Wrapper algorithm which are being used for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution. Our algorithm uses Open Directory Project (ODP) Taxonomy as data set for classification of pattern's and Radial Basis function to cluster the pattern which in turn avoids the problem of selecting distance and number of cluster's in case of K-means Clustering algorithm. The proposed Bayesian classifier identifies the user interest efficiently with less time complexity. Our classifier is well efficient than existing classifiers like svm and Ripper.

Keywords

• Data mining, neuron, dimension reduction, K-means clustering algorithm

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Cite this Article as Follows

Dr. J. Thirumaran. Clustering Techniques with Dimensionality Reduction for Data Mining. International Conference on Inter Disciplinary Research in Engineering and Technology 2014. Vol. 1. Chennai: Association of Scientists, Developers and Faculties, 2014. 82-85. Print.