By Topic

Integrating Clustering and Supervised Learning for Categorical Data Analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ujjwal Maulik ; Department of Computer Science and Engineering, Jadavpur University, Kolkata, India ; Sanghamitra Bandyopadhyay ; Indrajit Saha

The problem of fuzzy clustering of categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, is an important problem in exploratory data analysis. As a result, a few clustering algorithms with focus on categorical data have been proposed. In this paper, a modified differential evolution (DE)-based fuzzy c-medoids (FCMdd) clustering of categorical data has been proposed. The algorithm combines both local as well as global information with adaptive weighting. The performance of the proposed method has been compared with those using genetic algorithm, simulated annealing, and the classical DE technique, besides the FCMdd, fuzzy k-modes, and average linkage hierarchical clustering algorithm for four artificial and four real life categorical data sets. Statistical test has been carried out to establish the statistical significance of the proposed method. To improve the result further, the clustering method is integrated with a support vector machine (SVM), a well-known technique for supervised learning. A fraction of the data points selected from different clusters based on their proximity to the respective medoids is used for training the SVM. The clustering assignments of the remaining points are thereafter determined using the trained classifier. The superiority of the integrated clustering and supervised learning approach has been demonstrated.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans  (Volume:40 ,  Issue: 4 )