A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering
DNIS '02 Proceedings of the Second International Workshop on Databases in Networked Information Systems
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Core algorithms in the CLEVER system
ACM Transactions on Internet Technology (TOIT)
Extraction and classification of dense communities in the web
Proceedings of the 16th international conference on World Wide Web
Identifying the subject of small, sparsely linked collections from a web community
International Journal of Web Based Communities
Pruning attribute values from data cubes with diamond dicing
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Extraction and classification of dense implicit communities in the Web graph
ACM Transactions on the Web (TWEB)
Enumeration of isolated cliques and pseudo-cliques
ACM Transactions on Algorithms (TALG)
Detection of web communities from community cores
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
PlusDBG: web community extraction scheme improving both precision and pseudo-recall
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Linear-time enumeration of isolated cliques
ESA'05 Proceedings of the 13th annual European conference on Algorithms
A better strategy of discovering link-pattern based communities by classical clustering methods
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Extract and rank web communities
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
| Hi-index | 0.00 |
The Web harbors a large number of community structures. Early detection of community structures has many purposes such as reliable searching and selective advertising. In this paper we investigate the problem of extracting and relating the web community structures from a large collection of Web-pages by performing hyper-link analysis. The proposed algorithm extracts the potential community signatures by extracting the corresponding dense bipartite graph (DBG) structures from the given data set of web pages. Further, the proposed algorithm can also be used to relate the extracted community signatures. We report the experimental results conducted on 10 GB TREC (Text REtrieval Conference) data collection that contains 1.7 million pages and 21.5 million links. The results demonstrate that the proposed approach extracts meaningful community signatures and relates them.