CLUSTERING MULTI ATTRIBUTE SIMILARITY INDEX FOR CATEGORICAL DATA STREAMS

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Published: Jun 20, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i3.5931
Keywords:
K-Means, Uncertain One Class Classifier, Multi attribute, Support Vector Machine, Feature Representation.

Main Article Content

MANDRU DEENA BABBU
Krishna University
Dr.YK Sundara Krishna
Krishna University

Abstract

Data mining is an aggressively concept in information retrieval based on different attributes from different data sources. For effective data collection from data sources with respect to relevant data, one-class learning is required to perform labeled based classification with individual training sequences on attributes. In clustering, uncertain data with different data set visualization. Uncertain One Class Clustering (UOCC) with support vector machine to explore data summarization in terms of user preference. UOCC process single attributes from reliable data streams for inconsistent data. So that in this paper we propose Clustering with Multi-Attribute Framework (CMAF) to group multiple attributes to explore uncertain data from reliable data. CMAF construct matrix with different reliable attributes based on relevant features. Proposed approach defines effective data summarization for relevant data with attribute partitioning and constructs user profile based on relative attributes. Experimental results come out for proposed approach gives better and expressive results with comparison of state of art methods.

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Vol. 9 No. 3 (2018): May - June 2018
Section
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Author Biographies

MANDRU DEENA BABBU, Krishna University

Research Scholor(PhD), Department of Computer Science, Krishna University

Dr.YK Sundara Krishna, Krishna University

Principal, KRISHNA UNIVERSITY, Machilipatnam-521001.

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