NDLI: A New Heuristic Possibilistic Clustering Algorithm for Feature Selection

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A New Heuristic Possibilistic Clustering Algorithm for Feature Selection

Content Provider	Semantic Scholar
Author	Kacprzyk, Janusz Owsinski, Jan W. Viattchenin, Dimitri A.
Copyright Year	2014
Abstract	The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data pre- processing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by us- ing the conventional, well-known and widely employed method of principal component analysis (PCA). Conclu- sions and suggestions for future research are given.
Starting Page	40
Ending Page	46
Page Count	7
File Format	PDF HTM / HTML
DOI	10.14313/JAMRIS_2-2014/18
Alternate Webpage(s)	http://www.jamris.org/images/ISSUES/ISSUE-2014-02/Viattchenin_40_46.pdf
Alternate Webpage(s)	http://www.researchgate.net/profile/Dmitri_Viattchenin/publication/269015231_A_New_Heuristic_Possibilistic_Clustering_Algorithm_for_Feature_Selection/links/547d7ce60cf285ad5b089adc.pdf
Alternate Webpage(s)	https://doi.org/10.14313/JAMRIS_2-2014%2F18
Volume Number	8
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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