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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 preprocessing 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 using the conventional, well-known and widely employed method of principal component analysis (PCA). Conclusions and suggestions for future research are given. |
| File Format | PDF HTM / HTML |
| 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 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Benchmark (computing) Cluster analysis Data pre-processing Feature selection Genetic Selection Heuristic Information Preprocessor Principal component analysis Whole Earth 'Lectronic Link statistical cluster |
| Content Type | Text |
| Resource Type | Article |
