Modified Structural and Attribute Clustering Algorithm for Improving Cluster Quality in Data Mining: A Quality Oriented Approach

G Abel Thangaraja,Saravanan Venkataraman Tirumalai

doi:10.19026/rjaset.8.1203

G Abel Thangaraja, Saravanan Venkataraman Tirumalai

Open Access

https://doi.org/10.19026/rjaset.8.1203

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Abstract

The need of Data mining is because of the explosive growth of data from terabytes to petabytes. Data mining preprocess aims to produce the quality mining result in descriptive and predictive analysis. The quality of a clustering result depends on both the similarity measure used by the method and its implementation. A straightforward way to combine structural and attribute similarities is to use a weighted distance function. Clustering results are arrived based on attribute similarities. The clusters balance the attribute and structural similarities. The existing Structural and Attribute cluster algorithm is analyzed and a new algorithm is proposed. Both the algorithms are compared and results are analyzed. It is found that the modified algorithm gives better quality clusters.

Highlights

Data mining: The need of Data mining is because of the explosive growth of data from terabytes to petabytes
Each pair of scores is considered as vertex of a graph
The results for the modified structural and attribute clustering algorithm show that the cluster is of good quality when compared with the existing SA cluster

Summary

INTRODUCTION

Data mining: The need of Data mining is because of the explosive growth of data from terabytes to petabytes. The quality of a clustering result depends on both the similarity measure used by the method and its implementation. A unified framework based on Neighbourhood random walk is to integrate structural and attribute similarities. Vertex distances and similarities have been measured by random walk principle. The purpose of this problem is to partition the attributed graph into k clusters with intracluster attributes. This partitioning is complicated because attributed and structural similarities are independent. The techniques adopted in this study are listed below: Propose a unified Neighbourhood random walk distance measure to combine attribute and structural similarities.

LITERATURE REVIEW

METHODOLOGY

CONCLUSION