Title :
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
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;
Conference_Titel :
Computational Intelligence for Modelling Control & Automation, 2008 International Conference on
Conference_Location :
Vienna
Print_ISBN :
978-0-7695-3514-2
DOI :
10.1109/CIMCA.2008.34
