Dynamic Euclidean K-Means Clustering Algorithm in Data Mining
1G.Sivabharathi , Assistant Professor, Mangayarkarasi College of Arts and Science for Women, Madurai,
2Dr. K.Chitra Ph.D ,Assistant Professor, Government Arts College, Melur.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11129-11133 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9443118419/2019©BEIESP | DOI: 10.35940/ijrte.D9443.118419
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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: Data mining was the practice of processing data in order to derive interesting patterns as well as designs from the system used to analyze data. Grouping was the process of grouping artifacts even though that items in almost the same category are more identical than items in other classes. The existing system main drawbacks are not able to show clear logical information about the market analysis and cannot summarize the strength, weakness, opportunities and threats. Among these clustering is considered as a significant technique to capture the structure of data. Data mining adds to clustering is complicated to retrieve Wide databases with either a variety of different forms of attributes. This includes special specific clustering strategies with Euclidean K-Means grouping process. The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. In this technique the threshold value is used to determine the information is the same category or even a new team is formed. Proposing an Euclidean K-means algorithm is a necessity. The squared Euclidean distance metric results of the suggested algorithm are tested in this journal experimental results. Distance metrics are used to build reliable features and functionality including grouping for data mining. The simulation process is carried out in MATLAB tool and outperforms the proposed results. Keywords:
Keywords: Data Mining, Clustering, Market Analysis, Industrial Analysis, K-Means Clustering.
Scope of the Article: Data Mining.