Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Creating a Web community chart for navigating related communities
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Naviz: Website Navigational Behavior Visualizer
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Unified Framework for Clustering Heterogeneous Web Objects
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
Correlation-based Document Clustering using Web Logs
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 5 - Volume 5
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Optimal distance bounds for fast search on compressed time-series query logs
ACM Transactions on the Web (TWEB)
QUBiC: An adaptive approach to query-based recommendation
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
It the becomes possible that users can get kinds of information by just inputting search keyword(s) representing the topic which users are interested in. But it is not always true that users can hit upon search keyword(s) properly. In this paper, by using Web access logs (called panel logs), which are collected URL histories of Japanese users (called panels) selected without static deviation similar to the survey on TV audience rating, we study the methods of clustering search keywords. Different from the existing systems where the related search keywords are extracted based on the set of URLs viewed by the users after input of their original search keyword(s), we propose two novel methods of clustering the search words. One is based on the Web communities (set of similar web pages); the other is based on the set of nouns obtained by morphological analysis of Web pages. According to evaluation results, our proposed methods can extract more related search keywords than that based on URL.