Innovative Algorithm for Managing the Number of Clusters
Boumedyen Shannaq1, Ibrahim Rashid Al Shamsi2, Fouad Jameel Ibrahim AlAzzawi3
1Dr. Boumedyen Shannaq*, MIS, Business College, University of Burimi, Al Burimi, Oman.
2Dr. Ibrahim Rashid Al Shamsi, Business College, University of Burimi, Al Burimi, Oman.
3Dr. Fouad Jameel Ibrahim AlAzzawi, Iraq, Faculty Member- Al-Rafidain University College.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 310-315 | Volume-8 Issue-5, January 2020. | Retrieval Number: E4875018520/2020©BEIESP | DOI: 10.35940/ijrte.E4875.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Clustering, Knowledge Management, Business Value, Segmentation Strategy..
Scope of the Article: Data Management, Exploration, and Mining.