webminingresearchasurvey web mining research: a survey raymond kosala and hendrik blockeel acm...
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WebWeb MiningMining ResearchResearch: AA SurveySurvey
Raymond Kosala and Hendrik Blockeel
ACM SIGKDD, July 2000
Presented by Shan Huang, 4/24/2007
Revised and presented by Fan Min, 4/22/2009
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Outline
Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Four Problems
Finding relevant information Low precision and unindexed information
Creating new knowledge out of available information on the web
Personalizing the information Catering to personal preference in content and presentation
Learning about the consumers What does the customer want to do? Using web data to effectively market products and/or services
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Other Approaches
Web mining is NOT the only approach Database approach (DB) Information retrieval (IR) Natural language processing (NLP)
In-depth syntactic and semantic analysis
Web document community Standards, manually appended meta-information,
maintained directories, etc
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Direct vs. Indirect Web Mining
Web mining techniques can be used to solve the information overload problems: Directly
Attack the problem with web mining techniquesE.g. newsgroup agent classifies news as relevant
IndirectlyUsed as part of a bigger application that addresses
problemsE.g. used to create index terms for a web search service
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The Research
Converging research from: Database, information retrieval, and artificial intelligence (specifically NLP and machine learning)
Focusing on research from the machine learning point of view
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Outline
Introduction Web MiningWeb Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Web Mining: Definition
“Web mining refers to the overall process of discovering potentially useful and previously unknown information or knowledge from the Web data.” Can be viewed as four subtasks Not the same as Information Retrieval Not the same as Information Extraction
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Web Mining: Subtasks
Resource finding Retrieving intended documents
Information selection/pre-processing Select and pre-process specific information from selected
documents Generalization
Discover general patterns within and across web sites Analysis
Validation and/or interpretation of mined patterns
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Web Mining: Not IR
Information retrieval (IR) is the automatic retrieval of all relevant documents while at the same time retrieving as few of the non-relevant documents as possible
Web document classification, which is a Web Mining task, could be part of an IR system (e.g. indexing for a search engine)
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Web Mining: Not IE
Information extraction (IE) aims to extract the relevant facts from given documents IE systems for the general Web are not feasible Most focus on specific Web sites or content
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Web Mining and Machine Learning
Machine learning is concerned with the development of algorithms and techniques that allow computers to "learn".
Web mining is NOT learning from the Web. Some applications of machine learning on the web
are NOT Web Mining Methods used for Web Mining are NOT limited to
machine learning Oops, there is a close relationship between web
mining and machine learning
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Web Mining: The Agent Paradigm
User Interface Agents information retrieval agents, information
filtering agents, & personal assistant agents. Distributed Agents
distributed agents for knowledge discovery or data mining.
Problem solving by a group of agents Mobile Agents
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Web Mining: The Agent Paradigm
Content-based approach The system searches for items that match based
on an analysis of the content using the user preferences.
Collaborative approach The system tries to find users with similar
interests Recommendations given based on what similar
users did
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Outline
Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Web Mining Categories
Web Content Mining Discovering useful information from web
contents/data/documents.
Web Structure Mining Discovering the model underlying link structures (topology)
on the Web. E.g. discovering authorities and hubs
Web Usage Mining Make sense of data generated by surfers Usage data from logs, user profiles, user sessions, cookies,
user queries, bookmarks, mouse clicks and scrolls, etc.
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Web Content Data Structure
Unstructured – free text Semi-structured – HTML More structured – Table or Database
generated HTML pages Multimedia data – receive less attention than
text or hypertext
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Outline
Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Web Content Mining: IR View
Unstructured Documents Bag of words, or phrase-based feature
representation Features can be boolean or frequency based Features can be reduced using different feature
selection techniques Word stemming, combining morphological
variations into one feature
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Web Content Mining: IR View
Semi-Structured Documents Uses richer representations for features, based on
information from the document structure (typically HTML and hyperlinks)
Uses common data mining methods (whereas unstructured might use more text mining methods)
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Web Content Mining: DB View
Tries to infer the structure of a Web site or transform a Web site to become a database Better information management Better querying on the Web
Can be achieved by: Finding the schema of Web documents Building a Web warehouse Building a Web knowledge base Building a virtual database
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Web Content Mining: DB View
Mainly uses the Object Exchange Model (OEM) Represents semi-structured data (some structure, no rigid
schema) by a labeled graph
Process typically starts with manual selection of Web sites for content mining
Main application: building a structural summary of semi-structured data (schema extraction or discovery)
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Outline
Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Web Structure Mining
Interested in the structure between Web documents (not within a document)
Inspired by the study of social networks and citation analysis
Example: PageRank – Google Application: Discovering micro-communities in the
Web Measuring the “completeness” of a Web site
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Outline
Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Web Usage Mining
Tries to predict user behavior from interaction with the Web
Wide range of data (logs) Web client data Proxy server data Web server data
Two common approaches Map usage data into relational tables before using
adapted data mining techniques Use log data directly by utilizing special pre-processing
techniques
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Web Usage Mining
Typical problems: Distinguishing among unique users, server sessions, episodes, etc in the presence of caching and proxy servers
Often Usage Mining uses some background or domain knowledgeE.g. site topology, Web content, etc
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Web Usage Mining
Two main categories: Learning a user profile (personalized)
Web users would be interested in techniques that learn their needs and preferences automatically
Learning user navigation patterns (impersonalized)Information providers would be interested in
techniques that improve the effectiveness of their Web site or biasing the users towards the goals of the site
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Outline
Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions
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Conclusions
The paper tried to resolve confusion with regards to the term Web Mining Differentiated from IR and IE
Suggest three Web mining categories Content, Structure, and Usage Mining
Briefly described approaches for the three categories
Explored connection with agent paradigm
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Exam Question #1
Question: Outline the main characteristics of Web information.
Answer: Web information is huge, diverse, and dynamic.
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Exam Question #2
Question: How data mining techniques can be used in Web information analysis? Give at least two examples. Classification: classification on server logs using
decision tree, Naïve-Bayes classifier to discover the profiles of users belonging to a particular class
Clustering: Clustering can be used to group users exhibiting similar browsing patterns.
Association Analysis: association analysis can be used to relate pages that are most often referenced together in a single server session.
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Exam Question #3
Question: What are the three main areas of interest for Web mining?
Answer: (1) Web Content
(2) Web Structure
(3) Web Usage
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