1. Web Document Clustering: A Feasibility Demonstration Oren Zamir and Oren Etzioni, SIGIR, 1998

2. Introduction Clustering web search results Snippets vs. web pages “fast” algorithm Post-retrieval document browsing technique 2Talk on: "Web Document Clustering: A Feasibility Demonstration"

3. Suffix Tree Clustering(STC) STC is a linear time clustering algorithm that is based on a suffix tree which efficiently identifies sets of documents that share common phrases. STC satisfies the key requirements: STC treats a document as a string, making use of proximity information between words. STC is novel, incremental, and O(n) time algorithm. STC succinctly summarizes clusters’ contents for users. smaller set Quick because of working on smaller set of documents, incremantality …

4. Operating procedure of STC Step1: Document “cleaning” Html -> plain text Words stemming Mark sentence boundaries Remove non-word tokens Step 2: Identifying Base Clusters Step3: Combining Base Clusters

5. Step2: Identifying base Clusters — Suffix Tree * STC treats a document as a set of strings… Suffix tree of string S: a compact tree containing all the suffixes of S String1: “cat ate cheese”, String2: “mouse ate cheese too” String3: “cat ate mouse too”

6. Base cluster 6 Base cluster scoring: s(B) = |B| ⋅ f(|P|) Talk on: "Web Document Clustering: A Feasibility Demonstration"

7. Base clusters Base clusters corresponding to the suffix tree nodes

8. Cluster score s(B) = |B| * f(|P|) |B| is the number of documents in base cluster B |P| is the number of words in P that have a non- zero score zero score words: stopwords, too few( 40%)

9. Step 3:Combining Base Clusters Merge base clusters with a high overlap in their document sets documents may share multiple phrases. Similarity of B m and B n (0.5 is paramter) 1 iff | B m  B n | / | B m | > 0.5 =and | B m  B n | / | B n | > 0.5 0 otherwise

10. Base Cluster Graph Node: cluster Edge: similarity between two clusters > 1 What if “ate” is in the stop word list?

11. STC is Incremental As each document arrives from the web, we “clean” it (linear with collection size) Add it to the suffix tree. Each node that is updated/created as a result of this is tagged(linear) Update the relevant base clusters and recalculate the similarity of these base clusters to the rest of k highest scoring base clusters(linear) Check any changes to the final clusters(linear) Score and sort the final clusters, choose top 10...(linear)

12. STC allows cluster overlap… Why overlap is reasonable? Why overlap is reasonable? a document often has 1+ topics a document often has 1+ topics STC allows a document to appear in 1+ clusters, since documents may share 1+ phrases with other documents STC allows a document to appear in 1+ clusters, since documents may share 1+ phrases with other documents But not too similar to be merged into one cluster.. But not too similar to be merged into one cluster..

13. Output 13Talk on: "Web Document Clustering: A Feasibility Demonstration"

14. Evaluation 10 queries with 200 documents/snippets each MetaCrawler Manually assigned relevance judgment  40 relevant document for each query Comparison to other clustering algorithms STC is superior Alteration of STC and the other algorithms Phrases and overlapping important for STC 14Talk on: "Web Document Clustering: A Feasibility Demonstration"

15. Properties of STC Relevance Group relevant docs separately from irrelevant ones Browsable Summaries Concise and accurate descriptions of clusters Overlap Documents can be in multiple clusters Snippet-tolerance High quality from snippets Speed “cluster up to 1,000 docs in a few seconds” Incrementality Start to cluster while receiving docs 15Talk on: "Web Document Clustering: A Feasibility Demonstration"

16. 16 Questions… Talk on: "Web Document Clustering: A Feasibility Demonstration"