Title :
Interval-Valued Centroids in K-Means Algorithms
Author :
Nordin, B. ; Chenyi Hu ; Chen, Bing ; Sheng, Victor S.
Author_Institution :
Dept. of Comput. Sci., Univ. of Central Arkansas, Conway, AR, USA
Abstract :
The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).
Keywords :
data mining; learning (artificial intelligence); pattern clustering; set theory; FCPS; data mining; fundamental clustering problem suite; initial interval centroids; interval peak method; interval-valued centroid; k-means algorithm; machine learning; quality improvement; Clustering algorithms; Data mining; MATLAB; Machine learning; Machine learning algorithms; Standards; Upper bound; Clustering; K-Means; interval computing;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4673-4651-1
DOI :
10.1109/ICMLA.2012.87
