Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Gadupudi Dakshayani
Sree Vidyanikethan Engineering College

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Clustering Large Data with Mixed Values Using Extended Fuzzy Adaptive Resonance Theory Asadi Srinivasulu; Gadupudi Dakshayani
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 3: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i3.pp617-628

Abstract

Clustering is one of the technique or approach in content mining and it is used for grouping similar items. Clustering software datasets with mixed values is a major challenge in clustering applications. The previous work deals with unsupervised feature learning techniques such as k-Means and C-Means which cannot be able to process the mixed type of data. There are several drawbacks in the previous work such as cluster tendency, partitioning, less accuracy and less performance. To overcome all those problems the extended fuzzy adaptive resonance theory (EFART) came into existence which indicates that the usage of fuzzy ART with some traditional approach. This work deals with mixed type of data by applying unsupervised feature learning for achieving the sparse representation to make it easier for clustering algorithms to separate the data. The advantages of extended fuzzy adaptive resonance theory are high accuracy, high performance, good partitioning, and good cluster tendency. This EFART adopts unsupervised feature learning which helps to cluster the large data sets like the teaching assistant evaluation, iris and the wine datasets. Finally, the obtained results may consist of clusters which are formed based on the similarity of their attribute type and values.
Co-Authors Asadi Srinivasulu