NDLI: A novel soft subspace clustering algorithm with noise detection for high dimensional datasets

Content Provider	Springer Nature Link
Author	Chitsaz, Elham Zolghadri Jahromi, Mansoor
Copyright Year	2015
Abstract	When dealing with high dimensional data, clustering faces the curse of dimensionality problem. In such data sets, clusters of objects exist in subspaces rather than in whole feature space. Subspace clustering algorithms have already been introduced to tackle this problem. However, noisy data points present in this type of data can have great impact on the clustering results. Therefore, to overcome these problems simultaneously, the fuzzy soft subspace clustering with noise detection (FSSC-ND) is proposed. The presented algorithm is based on the entropy weighting soft subspace clustering and noise clustering. The FSSC-ND algorithm uses a new objective function and update rules to achieve the mentioned goals and present more interpretable clustering results. Several experiments have been conducted on artificial and UCI benchmark datasets to assess the performance of the proposed algorithm. In addition, a number of cancer gene expression datasets are used to evaluate the performance of the proposed algorithm when dealing with high dimensional data. The results of these experiments demonstrate the superiority of the FSSC-ND algorithm in comparison with the state of the art clustering algorithms developed in earlier research.
Starting Page	4463
Ending Page	4472
Page Count	10
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	20
Issue Number	11
e-ISSN	14337479
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-07-02
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Subspace clustering Fuzzy clustering Noise clustering High dimensional data Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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