1. Web Mining: Phrase-based Document Indexing and Document Clustering Khaled Hammouda, Ph.D. Candidate Mohamed Kamel, Supervisor, PI PAMI Research Group University of Waterloo Waterloo, Ontario, Canada

2. 2 Phrase-based Document Indexing Document Index Graph Structure A model based on a digraph representation of the phrases in the document set A model based on a digraph representation of the phrases in the document set Nodes correspond to unique terms Nodes correspond to unique terms Edges maintain phrase representation Edges maintain phrase representation A phrase is a path in the graph A phrase is a path in the graph The model is an inverted list (terms  documents) The model is an inverted list (terms  documents) Nodes carry term weight information for each document in which they appear Nodes carry term weight information for each document in which they appear Shared phrases can be matched effeciently Shared phrases can be matched effeciently Phrase-based Features Phrases: more informative feature than individual words  local context matching Phrases: more informative feature than individual words  local context matching Represent sentences rather than words Represent sentences rather than words Facilitate phrase-matching between documents Facilitate phrase-matching between documents Achieves accurate document pair-wise similarity Achieves accurate document pair-wise similarity Avoid high-dimensionality of vector space model Avoid high-dimensionality of vector space model Allow incremental processing Allow incremental processing Document Index Graph

3. 3 Phrase-based Document Indexing Document Index Graph (internal structure) Document Index Graph (size scalability) Document Index Graph (time performance)

4. 4 Document Clustering using Cluster Similarity Histograms Similarity Histogram-based Clustering (SHC). Clusters are represented using concise statitsical representation called similarity histograms. Maximize clusters coherency by maintaining high similarity distributions in clusters histograms. Enhance a cluster any time by re-distributing documents among clusters. Both original and receiving clusters benefit from more tight similarity distributions. SHC algorithm is incremental. +ve documents: contribute to cluster cohesiveness -ve documents: contribute to cluster looseness -ve documents in one cluster could be +ve documents in another Redistribute documents among clusters such that the number of –ve documents is reduced in each cluster

5. 5 Document Clustering using Cluster Similarity Histograms (cont’d) SHC (time performance)

6. 6 Phrases as Document Features Effect of Phrase Similarity (F-measure)Effect of Phrase Similarity (Entropy) SHC Clustering Improvement (F-measure) SHC Clustering Improvement (Entropy) Document Clustering using Similarity Histograms

7. 7 Current Research Web Mining Multi-Agent System Cooperative agents work on mining web content Cooperative agents work on mining web content Agents can negotiate and exchange data to achieve better solutions Agents can negotiate and exchange data to achieve better solutions Implemented distributed clustering Implemented distributed clustering Based on multiple standards including XML, Web Services. Later will incorporate XML Topic Maps (XTM), Semantic Web and Ontologies to represent discovered clusters. Based on multiple standards including XML, Web Services. Later will incorporate XML Topic Maps (XTM), Semantic Web and Ontologies to represent discovered clusters.

8. 8 Publications Journal Publications K. Hammouda and M. Kamel, “Efficient Phrase-based Document Indexing for Web Document Clustering”, IEEE Transactions on Knowledge and Data Engineering. Accepted, September 2003. K. Hammouda and M. Kamel, “Efficient Phrase-based Document Indexing for Web Document Clustering”, IEEE Transactions on Knowledge and Data Engineering. Accepted, September 2003. K. Hammouda and M. Kamel, “Document Similarity Using a Phrase Indexing Graph Model”, Knowledge and Information Systems. Springer. Accepted, May 2003. K. Hammouda and M. Kamel, “Document Similarity Using a Phrase Indexing Graph Model”, Knowledge and Information Systems. Springer. Accepted, May 2003. Conference Publications K. Hammouda and M. Kamel, “Incremental Document Clustering Using Cluster Similarity Histograms”, The 2003 IEEE/WIC International Conference on Web Intelligence (WI 2003), pp. 597-601, Halifax, Canada, October 2003 K. Hammouda and M. Kamel, “Incremental Document Clustering Using Cluster Similarity Histograms”, The 2003 IEEE/WIC International Conference on Web Intelligence (WI 2003), pp. 597-601, Halifax, Canada, October 2003 K. Hammouda and M. Kamel, “Phrase-based Document Similarity Based on an Index Graph Model”, The 2002 IEEE International Conference on Data Mining (ICDM'02), pp. 203-210, Maebashi, Japan, December 2002. K. Hammouda and M. Kamel, “Phrase-based Document Similarity Based on an Index Graph Model”, The 2002 IEEE International Conference on Data Mining (ICDM'02), pp. 203-210, Maebashi, Japan, December 2002. Available at: Available at: http://pami.uwaterloo.ca/nav.php?site=pub&action=list&researcher=hammouda