Paper Info
Title
The Effect Of Noise And Outliers On Fuzzy Clustering Of High Dimensional Data
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.
Year
DOI
Venue
2016
10.5220/0006070601010108
PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE, VOL 2: FCTA
Keywords
Field
DocType
Fuzzy Clustering, c-Means Models, High Dimensional Data, Noise, Possibilistic Clustering
Fuzzy clustering,Clustering high-dimensional data,CURE data clustering algorithm,Correlation clustering,Pattern recognition,Computer science,Outlier,Artificial intelligence,Cluster analysis
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Ludmila Himmelspach1244.62
Stefan Conrad2168105.91