<article_id>JCOM_V4_N3_RP1</article_id>
<title>Data Mining Model Based on Interval-valued Clustering</title>
<author>M. Bhargavi</author>
<journal>Journal on Computer Science</journal>
<issn>2347–6141</issn>
<volume>4</volume>
<issue>3</issue>
<first_page>1</first_page>
<last_page>10</last_page>
<keywords>Iterative, Interval-valued, Clustering, Design of Algorithm</keywords>
<abstract>Discovering potential patterns from complex data is a hot research topic. In this paper, the author proposes an iterative data mining model based on "Interval-Value" clustering, "Interval-Interval" clustering, and "Interval-Matrix" clustering. "Interval-Value" clustering uses the features of interval data and digital threshold and designed by "Netting"→ "Type-I clustering"→"Type-II clustering"; "Interval-Interval" clustering uses the features of interval data and interval threshold and designed with interval medium clustering; "Interval-Matrix" clustering uses the features of interval data and matrix threshold and designed by matrix threshold clustering. Motivation of the author is to mine the interval-valued association rules for giving dataset, and the experimental study is conducted to verify the new data mining method. Experimental results show that the data mining model based on interval-valued clustering is feasible and effective.</abstract>
<date>September - November 2016</date>
<copyright>Copyright &copy; 2016 i-manager publications. All rights reserved.</copyright>
<publisher>i-manager Publications</publisher>
<article_url>http://www.imanagerpublications.com/Article.aspx?ArticleId=8283</article_url>