Manuscript received on May 14, 2020. | Revised Manuscript received on May 23, 2020. | Manuscript published on June 10, 2020. | PP: 340-346 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.H6417069820 | DOI: 10.35940/ijitee.H6417.069820
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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: In data mining ample techniques use distance based measures for data clustering. Improving clustering performance is the fundamental goal in cluster domain related tasks. Many techniques are available for clustering numerical data as well as categorical data. Clustering is an unsupervised learning technique and objects are grouped or clustered based on similarity among the objects. A new cluster similarity finding measure, which is cosine like cluster similarity measure (CLCSM), is proposed in this paper. The proposed cluster similarity measure is used for data classification. Extensive experiments are conducted by taking UCI machine learning datasets. The experimental results have shown that the proposed cosinelike cluster similarity measure is superior to many of the existing cluster similarity measures for data classification.
Keywords: Clustering numerical data, Clustering performance, Cosine like cluster similarity, Distance based measures.
Scope of the Article: Classification