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Data.Mining.C.8(Ii).Web Mining 570802461

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Margaret Wang
Margaret Wang
Technology
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[object Object],[object Object],[object Object]
Chapter 8. Mining Complex Types of Data (II) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mining the World-Wide Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Web Mining: A challenging task ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Web Mining Taxonomy Web Mining Web Structure Mining Web Content Mining Web Page Content Mining Search Result Mining Web Usage Mining General Access Pattern Tracking Customized Usage Tracking
Mining the World-Wide Web Web Structure Mining Web Content Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],Search Result Mining Web Usage Mining General Access Pattern Tracking Customized Usage Tracking Web Mining
Mining the World-Wide Web Web Usage Mining General Access Pattern Tracking Customized Usage Tracking Web Structure Mining Web Content Mining Web Page Content Mining ,[object Object],[object Object],[object Object],[object Object],Web Mining
Mining the World-Wide Web Web Structure Mining Web Content Mining Web Page Content Mining Search Result Mining Web Usage Mining ,[object Object],[object Object],[object Object],[object Object],Customized Usage Tracking Web Mining
Mining the World-Wide Web Web Usage Mining General Access Pattern Tracking ,[object Object],[object Object],[object Object],[object Object],Web Structure Mining Web Content Mining Web Page Content Mining Search Result Mining Web Mining
Mining the World-Wide Web Web Content Mining Web Page Content Mining Search Result Mining Web Usage Mining General Access Pattern Tracking Customized Usage Tracking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Web Mining
Chapter 8. Mining Complex Types of Data (II) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Web Structure Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Web Structure Analysis ,[object Object],[object Object],[object Object],[object Object]
Chapter 8. Mining Complex Types of Data (II) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Background: Social Network Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Network and the Web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Centrality ,[object Object],[object Object]
Measure of Centrality ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Prestige ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measure of P restige ,[object Object],[object Object],[object Object],[object Object],[object Object]
Rank P restige ,[object Object],[object Object],[object Object],[object Object],[object Object]
Measure of Rank P restige ,[object Object],[object Object],[object Object],[object Object],[object Object]
Intuition Idea for Rank Prestige ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PageRank Algorithm ,[object Object],[object Object],O j is the number of out-link of j
Matrix Notation ,[object Object],[object Object],[object Object],[object Object]
Transition Probability Matrix ,[object Object],[object Object]
Let us start … ,[object Object],[object Object],[object Object],[object Object]
Random Surfer ,[object Object],[object Object],[object Object]
An Example Web Hyperlink Graph
Improved PageRank ,[object Object],[object Object],[object Object],[object Object],[object Object]
Follow the Above Example
Final PageRank Algorithm ,[object Object]
Final PageRank Algorithm ,[object Object],[object Object]
Compute PageRank ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Advantages of PageRank ,[object Object],[object Object],[object Object],[object Object]
Chapter 8. Mining Complex Types of Data (II) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Another Aim: Web Structure Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object]
Background: Co-citation and Bibliographic Coupling ,[object Object],[object Object],[object Object],[object Object]
Co-citation ,[object Object],[object Object],Fig. Paper i and paper j are co-cited by paper k
Co-citation （共引证） ,[object Object],[object Object],[object Object],[object Object]
Bibliographic C oupling （文献联结） ,[object Object],[object Object],[object Object],[object Object],Fig. Both paper i and paper j cite paper k
Bibliographic C oupling ,[object Object],[object Object]
HITS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Authorities and Hubs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mining the Web's Link Structures ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mining the Web's Link Structures ,[object Object],[object Object],[object Object],… Authority page (red) … Hub page (yellow) Hubs Authorities
Define Authority and Hub Weight for Each Page For the page p : authority weight ; hub weight q 1 q 2 q 3 page p a[p]:= sum of h[q], for  q, q  p q 1 q 2 q 3 page p h[p]:= sum of a[q], for  q, p  q Better authority (hub) pages with larger a(h)-values
The HITS Algorithm d 1 d 2 d 4 d 3 ,[object Object],[object Object],[object Object],[object Object]
The HITS Algorithm ,[object Object],[object Object]
HITS in Matrix Form ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Computation of HITS ,[object Object],[object Object]
Relationships with C o-citation and B ibliographic C oupling ,[object Object],[object Object],[object Object],[object Object]
HITS ( H yperlink- I nduced T opic S earch) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Root Set ( 根集 ) and Base Set( 基集 ) ,[object Object],[object Object],[object Object],[object Object],base root
Step 1 of HITS: Create Base Set from Root Set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 1 of HITS: Create Base Set from Root Set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The HITS Algorithm d 1 d 2 d 4 “ Adjacency matrix” d 3 Initial values: a=h=1 Iterate Normalize:
Step 2 of HITS: Calculate Authority and Hub Weight for Each Page ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 3 of HITS: Filter out the top authorities and hubs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Strengths and W eaknesses of HITS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chapter 8. Mining Complex Types of Data (II) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data.Mining.C.8(Ii).Web Mining 570802461

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Data.Mining.C.8(Ii).Web Mining 570802461

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  • 5. Web Mining Taxonomy Web Mining Web Structure Mining Web Content Mining Web Page Content Mining Search Result Mining Web Usage Mining General Access Pattern Tracking Customized Usage Tracking
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  • 30. An Example Web Hyperlink Graph
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  • 48. Define Authority and Hub Weight for Each Page For the page p : authority weight ; hub weight q 1 q 2 q 3 page p a[p]:= sum of h[q], for  q, q  p q 1 q 2 q 3 page p h[p]:= sum of a[q], for  q, p  q Better authority (hub) pages with larger a(h)-values
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  • 58. The HITS Algorithm d 1 d 2 d 4 “ Adjacency matrix” d 3 Initial values: a=h=1 Iterate Normalize:
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