On the use of variable-size fuzzy clustering for classification

Authors:
Vicenç Torra;Sadaaki Miyamoto
Affiliations:
Institut d'Investigació en Intel.ligència Artificial, Bellaterra, Catalonia, Spain;Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan
Venue:
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Year:
2006

Citing 3
Cited 1

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning

Machine Learning
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Editorial: New fuzzy c-means clustering model based on the data weighted approach

Data & Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Hard c-means can be used for building classifiers in supervised machine learning. For example, in a n-class problem, c clusters are built for each of the classes. This results into n . c centroids. Then, new examples can be classified according to the nearest centroid. In this work we consider the problem of building classifiers using fuzzy clustering techniques. In particular, we consider the use of fuzzy c-means, as well as some variations. Namely, fuzzy c-means with variable size and entropy based fuzzy c-means.