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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) s. 1 - 6
Hlavní autoři: Varshney, Ayush K., Danish Lohani, Q. M., Muhuri, Pranab K.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2020
Edice:IEEE International Conference on Fuzzy Systems
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISBN:1728169321
9781728169330
172816933X
9781728169323
ISSN:1558-4739
DOI:10.1109/FUZZ48607.2020.9177574