Title :
Keen-Means: A Web Page Clustering Tool Based on an Self-Adjustable K-Means Algorithm
Author :
Chun Hsiung Tseng ; Yung Hui Chen ; Chu Chun Chuang ; Jia Hua Wu ; Yi Syuan Yang ; Ya Wen Liang
Author_Institution :
Dept. of Inf. Manage., Nanhua Univ., Chiayi, Taiwan
Abstract :
Search engines usually do their jobs well. However, due to the fact that most existing search algorithms are keyword-based, search engines may not work as expected in some scenarios when ambiguity problems are encountered. A possible approach to overcome it is to categorize Web resources in advance. In this research, a k-means variation, the keen-means algorithm, along with its implementation is proposed. The algorithm will dynamically and automatically adjust the k value to achieve better results.
Keywords :
Internet; pattern clustering; search engines; Web page clustering tool; Web resources; k-means variation; keen-means algorithm; keyword-based search engines; search algorithms; self-adjustable k-means algorithm; Algorithm design and analysis; Clustering algorithms; Google; Labeling; Search engines; Web pages; Web services; Web information extraction; clustering; semantic Web;
Conference_Titel :
Ubi-Media Computing and Workshops (UMEDIA), 2014 7th International Conference on
Conference_Location :
Ulaanbaatar
Print_ISBN :
978-1-4799-4267-1
DOI :
10.1109/U-MEDIA.2014.44