Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Information retrieval
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Syntactic clustering of the Web
Selected papers from the sixth international conference on World Wide Web
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
A vector space model for automatic indexing
Communications of the ACM
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A survey of web caching schemes for the Internet
ACM SIGCOMM Computer Communication Review
Cluster validity methods: part I
ACM SIGMOD Record
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Web Document Classification Based on Fuzzy Association
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Mining longest repeating subsequences to predict world wide web surfing
USITS'99 Proceedings of the 2nd conference on USENIX Symposium on Internet Technologies and Systems - Volume 2
Architecture design of grid GIS and its applications on image processing based on LAN
Information Sciences—Informatics and Computer Science: An International Journal
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Mining web browsing patterns for E-commerce
Computers in Industry
A web-page recommender system via a data mining framework and the Semantic Web concept
International Journal of Computer Applications in Technology
System design and implementation of digital-image processing using computational grids
Computers & Geosciences
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Rank order-based recommendation approach for multiple featured products
Expert Systems with Applications: An International Journal
Learning latent variable models from distributed and abstracted data
Information Sciences: an International Journal
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Due to the inherently distributed nature of many networks, including the Internet, information and knowledge are generated and organized independently by different groups of people. To discover and exploit all the knowledge from different sources, a method of knowledge integration is usually required. Considering the document category sets as information sources, we define a problem of information integration called category merging. The purpose of category merging is to automatically construct a unified category set which represents and exploits document information from several different sources. This merging process is based on the clustering concept where categories with similar characteristics are merged into the same cluster under certain distributed constraints. To evaluate the quality of the merged category set, we measure the precision and recall values under three classification methods, Naive Bayes, Vector Space Model, and K-Nearest Neighbor. In addition, we propose a performance measure called cluster entropy, which determines how well the categories from different sources are distributed over the resulting clusters. We perform the merging process by using the real data sets collected from three different Web directories. The results show that our merging process improves the classification performance over the non-merged approach and also provides a better representation for all categories from distributed directories.