|
Abstract
|
Article Information:
Modified Structural and Attribute Clustering Algorithm for Improving Cluster Quality in Data Mining: A Quality Oriented Approach
G. Abel Thangaraja and Saravanan Venkataraman Tirumalai
Corresponding Author: G. Abel Thangaraja
Submitted: June 08, 2014
Accepted: July 13, 2014
Published: November 25, 2014 |
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
Key words: Attribute similarity, cluster quality, data mining, structural, , ,
|
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
G. Abel Thangaraja and Saravanan Venkataraman Tirumalai, . Modified Structural and Attribute Clustering Algorithm for Improving Cluster Quality in Data Mining: A Quality Oriented Approach. Research Journal of Applied Sciences, Engineering and Technology, (20): 2097-2102.
|
|
|
|
 |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
 |
| Information |
|
|
|
|
| Sales & Services |
|
|
|
|
