Data mining Application of Data Reduction and Clustering Domain of Textile Database
M. Salomi1, R. Lakshmi Priya2, Manimannan G3, N. Manjula Devi4
1M. Salomi, Assistant Professor, Department of Statistics, Madras Christian College, Chennai (Tamil Nadu), India.
2R. Lakshmi Priya, Assistant Professor, Department of Statistics, Dr. Ambedkar Govt. Arts College, Chennai (Tamil Nadu), India.
3Manimannan G, Assistant Professor, Department of Mathematics, TMG College of Arts and Science, Chennai (Tamil Nadu), India.
4N. Manjula Devi, Bio Statistician, Department of Community Medicine, Karpaka Vinayakar Institute of Medical Sciences, Chengalpet, (Tamil Nadu), India.
Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP: 228-232 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.D4921119420 | DOI: 10.35940/ijrte.D4921.119420
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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 paper attempts to identify the textile data structure and hidden pattern of original database with certain important parameters. The main objectives of this study are to identify the first n number of factors that explained over the study period. Initially factor analysis is performed to extract factor scores. Principal extraction is performed through Data mining package with sixteen textile fabrics parameters. Factor extraction is aimed to uncover the intrinsic pattern among the textile parameters considered and an important point of factor analysis is to extract factor scores for further investigation. Thus, factor analysis consistently resulted in three factors for the whole datasets. The amount of total variation explained is over 75 percent in factor analysis with varimax rotation. The factor loadings or factor structure matrix with unassociated rotation methods are not always easy to interpret. The nonhierarchical k-mean clustering is also used to identify meaningful cluster based on their parameter means of original database.
Keywords: Data Mining, Principal Component Analysis, k-mean Clustering, Sillohoutte plot and Scatter plot.