Authors:
Ludmila Himmelspach
and
Stefan Conrad
Affiliation:
Heinrich-Heine-University Duesseldorf, Germany
Keyword(s):
Fuzzy Clustering, c-Means Models, High Dimensional Data, Noise, Possibilistic Clustering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Information Retrieval and Data Mining
;
Fuzzy Systems
;
Pattern Recognition: Fuzzy Clustering and Classifiers
;
Soft Computing
Abstract:
Clustering high dimensional data is still a challenging problem for fuzzy clustering algorithms because distances between each pair of data items get similar with the increasing number of dimensions. The presence of noise and outliers in data is an additional problem for clustering algorithms because they might affect the computation of cluster centers. In this work, we analyze the effect of different kinds of noise and outliers on fuzzy clustering algorithms that can handle high dimensional data: FCM with attribute weighting, the multivariate fuzzy c-means (MFCM), and the possibilistic multivariate fuzzy c-means (PMFCM). Additionally, we propose a new version of PMFCM to enhance its ability handling noise and outliers in high dimensional data. The experimental results on different high dimensional data sets show that the possibilistic versions of MFCM produce accurate cluster centers independently of the kind of noise and outliers.