Title :
A Data Clustering Tool with Cluster Validity Indices
Author :
Qiao, Haiyan ; Edwards, Brandon
Author_Institution :
Dept. of Comput. Sci. & Eng., California State Univ. San Bernardino, San Bernardino, CA, USA
Abstract :
Data clustering is an important procedure to detect hidden patterns of a data set in a variety of fields, yet clustering analysis is a challenging problem, because many factors play together in devising and selecting a well tuned clustering technique and there are no predefined classes or examples to show whether the clusters are valid or not. In this paper, cluster validation methods are reviewed, and an extended tool with validation indices is developed.
Keywords :
data analysis; reviews; statistical analysis; unsupervised learning; cluster validation methods; cluster validity indices; data clustering tool; review; Algorithm design and analysis; Clustering algorithms; Clustering methods; Computer science; Data engineering; Information analysis; Partitioning algorithms; Pattern analysis; Statistics; Unsupervised learning; Cluster Validity Indices; Data Clustering Tool;
Conference_Titel :
Computing, Engineering and Information, 2009. ICC '09. International Conference on
Conference_Location :
Fullerton, CA
Print_ISBN :
978-0-7695-3538-8
DOI :
10.1109/ICC.2009.76
