A data mining method for refining groups in data using dynamic model based clustering

Author

Servi, Tayfun ; Erol, Hamza

Author_Institution

Dept. of Elementary Educ., Adiyaman Univ., Adyaman, Turkey

fYear

2013

fDate

19-21 June 2013

Firstpage

1

Lastpage

6

Abstract

A new data mining method is proposed for determining the number and structure of clusters, and refining groups in multivariate heterogeneous data set including groups, partly and completely overlapped group structures by using dynamic model based clustering. It is called dynamic model based clustering since the structure of model changes at each stage of refinement process dynamically. The proposed data mining method works without data reduction for high dimensional data in which some of variables including completely overlapped situations.

Keywords

data mining; pattern clustering; cluster number determination; cluster structure determination; completely-overlapped group structures; data mining method; dynamic model-based clustering; group refining; high-dimensional data; multivariate heterogeneous data set; partly-overlapped group structures; Classification tree analysis; Clustering algorithms; Computational modeling; Data mining; Data models; Glass; Refining; Data mining; dynamic model based clustering; refining groups in data;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on

Conference_Location

Albena

Print_ISBN

978-1-4799-0659-8

Type

conf

DOI

10.1109/INISTA.2013.6577645

Filename

6577645