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Software Implementation Methodology of Intelligent Information Systems of Learning and Knowledge Control (IISLKC) Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Interval Type-2 Fuzzy Possibilistic C-Means Clustering Algorithm

Authors : E. Rubio, Oscar Castillo, Patricia Melin

Published in: Recent Developments and New Direction in Soft-Computing Foundations and Applications

Publisher: Springer International Publishing

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Abstract

In this paper, we present the extension of the fuzzy possibilistic C-means (FPCM) algorithm using type-2 fuzzy logic techniques, with the goal of improving the performance of this algorithm. We also performed the comparison of this proposed algorithm against the interval type-2 fuzzy C-means (IT2FCM) algorithm to observe whether the proposed approach performs better than this algorithm. The proposed extension was realized considering both of the weight exponents (fuzzy and possibilistic), m and η, as interval fuzzy sets.

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previous chapter A Neural Network with a Learning Vector Quantization Algorithm for Multiclass Classification Using a Modular Approach
next chapter Fuzzy-Based Mechanisms for Selection and Recommendation Processes
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Metadata
Title
Interval Type-2 Fuzzy Possibilistic C-Means Clustering Algorithm
Authors
E. Rubio
Oscar Castillo
Patricia Melin
Copyright Year
2016
Book
Recent Developments and New Direction in Soft-Computing Foundations and Applications

Print ISBN: 978-3-319-32227-8

Electronic ISBN: 978-3-319-32229-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32229-2_14

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