A Clustering Algorithm Incorporating Density and Direction

Author

Song, Yu-Chen ; O´Grady, M.J. ; Hare, G. M P O ; Wang, Wei

Author_Institution

Inner Mongolia Univ. of Sci. & Technol., Baotou, China

fYear

2008

fDate

10-12 Dec. 2008

Firstpage

722

Lastpage

725

Abstract

This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm - Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design of the CADD algorithm. As an illustration of its applicability, CADD was used to cluster real world data from the geochemistry domain.

Keywords

data mining; pattern clustering; set theory; DENCLUE algorithm; K-means algorithm; clustering algorithm; clustering analysis automation; data mining; density-based methods; object density; object direction; partitioning methods; Algorithm design and analysis; Clustering algorithms; Computer science; Data mining; Density functional theory; Design automation; Educational institutions; Informatics; Partitioning algorithms; User centered design; Algorithm design; CADD algorithm; CADD application;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling Control & Automation, 2008 International Conference on

Conference_Location

Vienna

Print_ISBN

978-0-7695-3514-2

Type

conf

DOI

10.1109/CIMCA.2008.34

Filename

5172714