Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms
Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing con...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2015/238237 |
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| _version_ | 1832560387521773568 |
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| author | Arindam Chaudhuri |
| author_facet | Arindam Chaudhuri |
| author_sort | Arindam Chaudhuri |
| collection | DOAJ |
| description | Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio. |
| format | Article |
| id | doaj-art-669e262b0d304f1ab890a5f5cd54d9b0 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-669e262b0d304f1ab890a5f5cd54d9b02025-02-03T01:27:40ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2015-01-01201510.1155/2015/238237238237Intuitionistic Fuzzy Possibilistic C Means Clustering AlgorithmsArindam Chaudhuri0Samsung Research & Development Institute, Noida 201304, IndiaIntuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.http://dx.doi.org/10.1155/2015/238237 |
| spellingShingle | Arindam Chaudhuri Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms Advances in Fuzzy Systems |
| title | Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms |
| title_full | Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms |
| title_fullStr | Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms |
| title_full_unstemmed | Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms |
| title_short | Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms |
| title_sort | intuitionistic fuzzy possibilistic c means clustering algorithms |
| url | http://dx.doi.org/10.1155/2015/238237 |
| work_keys_str_mv | AT arindamchaudhuri intuitionisticfuzzypossibilisticcmeansclusteringalgorithms |