Open AccessFinding Best Clustering For Big Networks with Minimum Objective Function by Using Probabilistic Tabu Search
Author(s) -
Ali Falah Yaqoob,
Basad Al-Sarray
Publication year - 2019
Publication title -
iraqi journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.152
H-Index - 4
eISSN - 2312-1637
pISSN - 0067-2904
DOI - 10.24996/ijs.2019.60.8.21
Subject(s) - tabu search , cluster analysis , probabilistic logic , heuristic , fuzzy logic , function (biology) , computer science , fuzzy clustering , mathematics , cluster (spacecraft) , mathematical optimization , data mining , algorithm , artificial intelligence , evolutionary biology , biology , programming language
Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and cluster’s number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.