Feature-Weighted Possibilistic c-Means Clustering With a Feature-Reduction Framework

In 1993, Krishnapuram and Keller proposed possibilistic c -means (PCM) clustering, where the PCM had various extensions in the literature. However, the PCM algorithm with its extensions treats data points under equal importance for features. In real applications, different features in a dataset shou...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2021-05, Vol.29 (5), p.1093-1106
Main Authors: Yang, Miin-Shen, Benjamin, Josephine B. M.
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Computational complexity
Data points
Datasets
Entropy
feature weights
feature-reduction schema
feature-weighted PCM (FW-PCM)
feature-weighted reduction PCM (FW-R-PCM)
Linear programming
Noise measurement
Phase change materials
possibilistic <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c -means (PCM)
Reduction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In 1993, Krishnapuram and Keller proposed possibilistic c -means (PCM) clustering, where the PCM had various extensions in the literature. However, the PCM algorithm with its extensions treats data points under equal importance for features. In real applications, different features in a dataset should take different importance with different weights. In this article, we first propose a feature-weighted PCM (FW-PCM). We then construct a feature-reduction framework. Therefore, we give a feature-weighted possibilistic c -means clustering with a feature-reduction framework, termed as a feature-weighted reduction PCM (FW-R-PCM) algorithm. The proposed FW-R-PCM can improve the clustering performance of PCM by calculating feature weights to identify important features, and so it can consequently eliminate these irrelevant features to reduce feature dimension. Its theoretical behavior and computational complexity are also analyzed. The effectiveness and usefulness of FW-R-PCM are demonstrated through experimental results using synthetic and real datasets, where comparisons of FW-R-PCM with PCM, FW-PCM, and some existing feature-weighted clustering algorithms are also made.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.2968879