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Visual Data Mining Based on Partial Similarity Concepts

Visual Data Mining Based on Partial Similarity Concepts

Juliusz L. Kulikowski
Copyright: © 2009 |Pages: 16
ISBN13: 9781605661889|ISBN10: 1605661880|ISBN13 Softcover: 9781616926021|EISBN13: 9781605661896
DOI: 10.4018/978-1-60566-188-9.ch007
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MLA

Kulikowski, Juliusz L. "Visual Data Mining Based on Partial Similarity Concepts." Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, et al., IGI Global, 2009, pp. 166-181. https://doi.org/10.4018/978-1-60566-188-9.ch007

APA

Kulikowski, J. L. (2009). Visual Data Mining Based on Partial Similarity Concepts. In D. Tao, D. Xu, & X. Li (Eds.), Semantic Mining Technologies for Multimedia Databases (pp. 166-181). IGI Global. https://doi.org/10.4018/978-1-60566-188-9.ch007

Chicago

Kulikowski, Juliusz L. "Visual Data Mining Based on Partial Similarity Concepts." In Semantic Mining Technologies for Multimedia Databases, edited by Dacheng Tao, Dong Xu, and Xuelong Li, 166-181. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-188-9.ch007

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Abstract

Visual data mining is a procedure aimed at a selection from a document’s repository subsets of documents presenting certain classes of objects; the last may be characterized as classes of objects’ similarity or, more generally, as classes of objects satisfying certain relationships. In this chapter attention will be focused on selection of visual documents representing objects belonging to similarity classes.

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