Innovative Algorithm for Managing the Number of Clusters
Author(s) -
Boumedyen Shannaq,
Dr.Ibrahim Rashid Al Shamsi,
Dr.Fouad Jameel Ibrahim AlAzzawi
Publication year - 2020
Publication title -
international journal of recent technology and engineering (ijrte)
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e4875.018520
Subject(s) - cluster analysis , task (project management) , computer science , cluster (spacecraft) , artificial neural network , set (abstract data type) , data mining , fuzzy clustering , value (mathematics) , group (periodic table) , fuzzy logic , algorithm , artificial intelligence , machine learning , engineering , chemistry , systems engineering , organic chemistry , programming language
This research work proposed an integrated approach using Fuzzy Clustering to discover the optimal number of clusters. The proposed technique is a great technological innovation clustering algorithm in marketing and could be used to determine the best group of customers, similar items and products. The new approach can independently determine the initial distribution of cluster centers. The task of finding the number of clusters is converted into the task of determining the size of the neural network, which later translated to identify the optimal groups of clusters. This approach has been tested using four business data set and shows outstanding results compared to traditional approaches. The proposed method is able to find without any significant error the expected exact number of clusters. Further, we believe that this work is a business value to increase market efficiency in finding out what group of clusters is more cost-effective.
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