Web Page Clustering via Partition Adaptive Affinity Propagation

Authors:
Changyin Sun;Yifan Wang;Haina Zhao
Affiliations:
College of Electrical Engineering, Hohai University, Nanjing, China 210098 and School of Automation, Southeast University, Nanjing, China 210096;College of Electrical Engineering, Hohai University, Nanjing, China 210098;College of Electrical Engineering, Hohai University, Nanjing, China 210098
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

Citing 7
Cited 1

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Journal of Computational and Applied Mathematics
Term-weighting approaches in automatic text retrieval

Information Processing and Management: an International Journal
Data clustering: a review

ACM Computing Surveys (CSUR)
Information Retrieval

Information Retrieval
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering

Machine Learning
A local approach of adaptive affinity propagation clustering for large scale data

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An overview of web data clustering practices

EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology

Exploring functional connectivity networks in fMRI data using clustering analysis

BI'11 Proceedings of the 2011 international conference on Brain informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering techniques have been applied to categorize documents on Web and extract knowledge from Web. In this paper, we introduce a novel clustering method into Web page clustering, which is an extension of affinity propagation (AP). This method is called partition adaptive affinity propagation (PAAP), which can automatically rerun AP procedure to yield optimal clustering results and eliminate number oscillations if they occur. Experiments are carried out to compare PAAP with K-means and AP on ten different Web page data sets. The results verify that PAAP can find better clusters when compared with similar methods. And the results also demonstrate that PAAP is robust and effective when clustering Web pages.