Improved Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: Improved PIFCM

Recently proposed Probabilistic Intuitionistic Fuzzy c-Means Algorithm (PIFCM) is a Probabilistic Euclidian distance measure (PEDM) based clustering technique, which incorporate computation of probabilistic intervals (P ij , Q ij ) for each of the data point. PIFCM algorithm employs a random members...

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Veröffentlicht in:2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) S. 1 - 6
Hauptverfasser: Varshney, Ayush K., Danish Lohani, Q. M., Muhuri, Pranab K.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2020
Schriftenreihe:IEEE International Conference on Fuzzy Systems
Schlagworte:
AIFS based clustering
Clustering algorithms
Computer Science
Conferences
datalogi
Distributed databases
Fuzzy clustering
Fuzzy systems
IFCM
Limiting
PEDM
PIFCM
probabilistic interval
Probabilistic logic
ISBN:1728169321, 9781728169330, 172816933X, 9781728169323
ISSN:1558-4739
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Abstract Recently proposed Probabilistic Intuitionistic Fuzzy c-Means Algorithm (PIFCM) is a Probabilistic Euclidian distance measure (PEDM) based clustering technique, which incorporate computation of probabilistic intervals (P ij , Q ij ) for each of the data point. PIFCM algorithm employs a random membership function \frac{1}{{\left| x \right|}} and discards a data point if its membership value is uniformly distributed in the clusters. Fuzzy clustering always gets affected by the choice of the membership function. Accordingly, in PIFCM algorithm, membership function changes the properties of the data limiting its capabilities in giving consistent clustering results. Moreover, PIFCM algorithm incorporates computation of redundant matrices while finding P ij and Q ij . In this paper, we propose some novel changes in the existing PIFCM algorithm, and hence introduce our Improved PIFCM algorithm. The improved PIFCM algorithm considers the min-max normalization as membership function, and also removes the redundant matrix computation that was used to find the P ij and Q ij in the original PIFCM. Results over various UCI datasets validates the superiority of our improved PIFCM algorithm over FCM algorithm, IFCM algorithm and PIFCM algorithm.
AbstractList Recently proposed Probabilistic Intuitionistic Fuzzy c-Means Algorithm (PIFCM) is a Probabilistic Euclidian distance measure (PEDM) based clustering technique, which incorporate computation of probabilistic intervals (P ij , Q ij ) for each of the data point. PIFCM algorithm employs a random membership function 1/|x| and discards a data point if its membership value is uniformly distributed in the clusters. Fuzzy clustering always gets affected by the choice of the membership function. Accordingly, in PIFCM algorithm, membership function changes the properties of the data limiting its capabilities in giving consistent clustering results. Moreover, PIFCM algorithm incorporates computation of redundant matrices while finding P ij and Q ij . In this paper, we propose some novel changes in the existing PIFCM algorithm, and hence introduce our Improved PIFCM algorithm. The improved PIFCM algorithm considers the min-max normalization as membership function, and also removes the redundant matrix computation that was used to find the P ij and Q ij in the original PIFCM. Results over various UCI datasets validates the superiority of our improved PIFCM algorithm over FCM algorithm, IFCM algorithm and PIFCM algorithm.
Recently proposed Probabilistic Intuitionistic Fuzzy c-Means Algorithm (PIFCM) is a Probabilistic Euclidian distance measure (PEDM) based clustering technique, which incorporate computation of probabilistic intervals (P ij , Q ij ) for each of the data point. PIFCM algorithm employs a random membership function \frac{1}{{\left| x \right|}} and discards a data point if its membership value is uniformly distributed in the clusters. Fuzzy clustering always gets affected by the choice of the membership function. Accordingly, in PIFCM algorithm, membership function changes the properties of the data limiting its capabilities in giving consistent clustering results. Moreover, PIFCM algorithm incorporates computation of redundant matrices while finding P ij and Q ij . In this paper, we propose some novel changes in the existing PIFCM algorithm, and hence introduce our Improved PIFCM algorithm. The improved PIFCM algorithm considers the min-max normalization as membership function, and also removes the redundant matrix computation that was used to find the P ij and Q ij in the original PIFCM. Results over various UCI datasets validates the superiority of our improved PIFCM algorithm over FCM algorithm, IFCM algorithm and PIFCM algorithm.
Author Muhuri, Pranab K.
Danish Lohani, Q. M.
Varshney, Ayush K.
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  givenname: Pranab K.
  surname: Muhuri
  fullname: Muhuri, Pranab K.
  organization: South Asian University,Department of Computer Science,New Delhi,India
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Snippet Recently proposed Probabilistic Intuitionistic Fuzzy c-Means Algorithm (PIFCM) is a Probabilistic Euclidian distance measure (PEDM) based clustering technique,...
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SubjectTerms AIFS based clustering
Clustering algorithms
Computer Science
Conferences
datalogi
Distributed databases
Fuzzy clustering
Fuzzy systems
IFCM
Limiting
PEDM
PIFCM
probabilistic interval
Probabilistic logic
Title Improved Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: Improved PIFCM
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