1. WebMiningResearchASurvey Web Mining Research: A Survey Authors: Raymond Kosala and Hendrik Blockeel ACM SIGKDD, July 2000 Computer Science Department University Of Vermont Revised and Presented by Onur Demircan

2. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 2Web Mining Research: A Survey

3. Introduction  With the huge amount of information available online, the World Wide Web is a fertile area for data mining research.  WWW is a popular and interactive medium to circulate information today.  The Web is huge, diverse, and dynamic. Thus raises the scalability, multimedia data, and temporal issues respectively. Web Mining Research: A Survey3

4. Four Problems  Finding relevant information Low precision and unindexed information  Creating new knowledge out of available information on the web A data-triggered process  Personalizing the information Personal preference in content and presentation of the information  Learning about the consumers What does the customer want to do? 4Web Mining Research: A Survey

5. Other Approaches Web mining is NOT the only approach  Database approach (DB)  Information retrieval (IR)  Natural language processing (NLP)  Machine Learning  Web document community 5Web Mining Research: A Survey

6. Direct vs. Indirect Web Mining  Web mining techniques can be used to solve the information overload problems:  Directly Address the problem with web mining techniques E.g. newsgroup agent classifies whether the news as relevant  Indirectly Used as part of a bigger application that addresses problems E.g. used to create index terms for a web search service 6Web Mining Research: A Survey

7. The Research  Converging research from: Database, information retrieval, and artificial intelligence (specifically NLP and machine learning)  Attempt to put research done in a structured way from the machine learning point of view 7Web Mining Research: A Survey

8. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 8Web Mining Research: A Survey

9. 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 9Web Mining Research: A Survey

10. Web Mining: Subtasks  Resource finding Retrieving intended web documents  Information selection and pre-processing Select and pre-process specific information from selected documents Kind of transformation processes of the original data retrieved in the IR process This transformation could be a kind of pre-processing  Generalization Discover general patterns within and across web sites  Analysis Validation and/or interpretation of mined patterns 10Web Mining Research: A Survey

11. Web Mining and Information Retrieval  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  Goal: Indexing text and searching for useful documents in a collection.  Research in IR: modeling, document classification and categorization, user interfaces, data visualization, filtering etc.  Web document classification, which is a Web Mining task, could be part of an IR system (e.g. indexing for a search engine) Viewed in this respect, Web mining is part of the (Web) IR process. 11Web Mining Research: A Survey

12. Web Mining and Information Extraction  Information Extraction (IE): Transforming a collection of documents, into information that is more easily understood and analyzed.  Building IE systems manually for the general Web are not feasible Most IE systems focus on specific Web sites or content to extract 12Web Mining Research: A Survey

13. Compare IR and IE  IR aims to select relevant documents IE aims to extract the relevant facts from given documents  IR views the text in a document just as a bag of unordered words IE interested in structure or representation of a document Web Mining Research: A Survey13

14. Web Mining and The Agent Paradigm  Web mining is often viewed from or implemented within an agent paradigm. Web mining has a close relationship with Intelligent Agents.  User Interface Agents information retrieval agents, information filtering agents, & personal assistant agents.  Distributed Agents Concerned with problem solving by a group of agents. distributed agents for knowledge discovery or data mining.  Mobile Agents 14Web Mining Research: A Survey

15. Web Mining and The Agent Paradigm (contd.)  Two frequently used approaches for developing intelligent agents:  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 to give recommendations to. Analyze the user profiles and sessions or transactions. 15Web Mining Research: A Survey

16. Agents based on Filtering Technology Web Mining Research: A Survey16

17. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 17Web Mining Research: A Survey

18. Web Mining Categories  Web Content Mining Discovering useful information from web page 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 Extraction of interesting knowledge from logging information produced by web servers. Usage data from logs, user profiles, user sessions, cookies, user queries, bookmarks, mouse clicks and scrolls, etc. 18Web Mining Research: A Survey

19. Web Mining Categories Web Mining Research: A Survey19

20. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 20Web Mining Research: A Survey

21. Web Content Data Structure  Web content consists of several types of data Text, image, audio, video, hyperlinks.  Unstructured – free text  Semi-structured – HTML  More structured – Data in the tables or database generated HTML pages Note: much of the Web content data is unstructured text data. 21Web Mining Research: A Survey21

22. Web Content Mining: IR View  Unstructured Documents Bag of words to represent unstructured documents Takes single word as feature Ignores the sequence in which words occur Features could be Boolean Word either occurs or does not occur in a document Frequency based Frequency of the word in a document Variations of the feature selection include Removing the case, punctuation, infrequent words and stop words Features can be reduced using different feature selection techniques: Information gain, mutual information, cross entropy. Stemming: which reduces words to their morphological roots. 22Web Mining Research: A Survey

23. Web Content Mining: IR View  Semi-Structured Documents Uses richer representations for features Due to the additional structural information in the hypertext document (typically HTML and hyperlinks) Uses common data mining methods (whereas unstructured might use more text mining methods) Application: Hypertext classification or categorization and clustering, learning relations between web documents, learning extraction patterns or rules, and finding patterns in semi-structured data. Web Mining Research: A Survey23

24. Web Content Mining: DB View  The database techniques on the Web are related to the problems of managing and querying the information on the Web.  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 24Web Mining Research: A Survey

25. Data Warehouse  A data warehouse maintains a copy of information from the source transaction systems.  This architectural complexity provides the opportunity to: Congregates data from multiple sources into a single database so a single query engine can be used to present data. Web Mining Research: A Survey25

26. Web Content Mining: DB View  DB view mainly uses the Object Exchange Model (OEM) Represents semi-structured data by a labeled graph The data in the OEM is viewed as a graph, with objects as the vertices and labels on the edges Each object is identified by an object identifier [oid] and Value is either atomic or complex  Process typically starts with manual selection of Web sites for doing Web content mining  Main application: The task of finding frequent substructures in semi-structured data The task of creating multi-layered database 26Web Mining Research: A Survey

27. What is Object Exchange Model(OEM)  An OEM data graph is a rooted, labelled, directed graph  Its edge labels map to strings  Only its leaf nodes have labels which map to data values  No ordering of edges leaving a node Web Mining Research: A Survey27

28. OEM Example Web Mining Research: A Survey28

29. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 29Web Mining Research: A Survey

30. Web Structure Mining  Interested in the structure of the hyperlinks within the Web  Inspired by the study of social networks and citation analysis Can discover specific types of pages(such as hubs, authorities, etc.) based on the incoming and outgoing links.  Application: Discovering micro-communities in the Web, measuring the “completeness” of a Web site 30Web Mining Research: A Survey

31. NETWORK GRAPH Web Mining Research: A Survey31

32. NETWORK GRAPH  Stefan Decker (along with Rudi Studer and Raphael Volz) plays the role of a local bridge between the Karlsruhe group and other parts of the core. Web Mining Research: A Survey32

33. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 33Web Mining Research: A Survey

34. 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 Maps the usage data of Web server into relational tables before an adapted data mining techniques Uses the log data directly by utilizing special pre-processing techniques 34Web Mining Research: A Survey

35. 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 knowledge E.g. site topology, Web content, etc. 35Web Mining Research: A Survey

36. Web Usage Mining  Applications: 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 36Web Mining Research: A Survey

37. Outline  Introduction  Web Mining  Web Content Mining  Web Structure Mining  Web Usage Mining  Conclusion & Exam Questions 37Web Mining Research: A Survey

38. Conclusions  Survey the research in the area of Web mining.  Suggest three Web mining categories Content, Structure, and Usage Mining And then situate some of the research with respect to these categories  Explored connection between Web mining categories and related agent paradigm 38Web Mining Research: A Survey

39. Exam Question #1  Question: Outline the main characteristics of Web information.  Answer:Web information is huge, diverse, and dynamic. 39Web Mining Research: A Survey

40. Exam Question #2  Question: Define Web Mining  Answer: Web mining refers to the overall process of discovering potentially useful and previously unknown information or knowledge from the Web data. 40Web Mining Research: A Survey

41. 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 41Web Mining Research: A Survey

42. Thank you!