Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans

While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans...

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Bibliographic Details
Published in:Fuzzy sets and systems Vol. 221; pp. 48 - 64
Main Authors: Liu, Chien-Liang, Chang, Tao-Hsing, Li, Hsuan-Hsun
Format: Journal Article
Language:English
Published: Elsevier B.V 16.06.2013
Subjects:
Algorithms
Clustering
Focusing
Fuzzy
Fuzzy clustering
Fuzzy logic
Fuzzy semi-Kmeans
Fuzzy set theory
Gaussian
Mathematical models
Semi-supervised learning
Text mining
Text mining
Fuzzy clustering
Semi-supervised learning
Fuzzy semi-Kmeans
ISSN:0165-0114, 1872-6801
Online Access:Get full text
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Summary:While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans provides the flexibility to employ different fuzzy membership functions to measure the distance between data. This work employs Gaussian weighting function to conduct experiments, but cosine similarity function can be used as well. This work conducts experiments on three data sets and compares fuzzy semi-Kmeans with several methods. The experimental results indicate that fuzzy semi-Kmeans can generally outperform the other methods.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2013.01.004