Web Usage Pattern
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This document discusses web usage mining. It begins by defining web mining and its three categories: web content mining, web structure mining, and web usage mining. The main focus is on web usage mining, which involves discovering user navigation patterns and predicting user behavior. The key processes of web usage mining are preprocessing raw data, pattern discovery using algorithms, and pattern analysis. Pattern discovery techniques discussed include statistical analysis, clustering, classification, association rules, and sequential patterns. Potential applications are personalized recommendations, system improvements, and business intelligence. The document concludes by discussing future research directions such as usage mining on the semantic web and analyzing discovered patterns.
Web Usage Pattern
- 1. Web Usage Pattern SHAH RUSHABH R CE-111 SHREYANSH R KEJRIWAL CE-113
- 2. Outline Brief overview of Web mining Web usage mining Application areas of Web usage mining Future research directions
- 3. Web Mining Web Mining is the application of data mining techniques to discover and retrieve useful information and patterns from the World Wide Web documents and services.
- 4. Web Mining Categories Web Content Mining- extracting knowledge from the content of the Web Web Structure Mining- discovering the model underlying the link structures of the Web Web Usage Mining- discovering user’s navigation pattern and predicting user’s behavior
- 5. Web Usage Mining Processes Preprocessing: conversion of the raw data into the data abstraction (users, sessions, episodes, clickstreams, and pageviews) necessary for further applying the data mining algorithm. Pattern Discovery: is the key component of WUM, which converges the algorithms and techniques from data mining, machine learning, statistics and pattern recognition etc. research categories. Pattern Analysis: Validation and interpretation of the mined patterns
- 6. Web Usage Mining Processes (Cont.)
- 7. Web Usage Mining- Preprocessing Data Cleaning: remove outliers and/or irrelative data User Identification: associate page references with different users Session Identification: divide all pages accessed by a user into sessions Path Completion: add important page access records that are missing in the access log due to browser and proxy server caching Formatting: format the sessions according to the type of data mining to be accomplished.
- 8. Web Usage Mining - Pattern Discovery Tasks Statistical Analysis: frequency analysis, mean, median, etc. ◦ Improve system performance ◦ Provide support for marketing decisions ◦ Simplify site modification task Clustering: ◦ Clustering of users help to discover groups of users with similar navigation patterns => provide personalized Web content ◦ Clustering of pages help to discover groups of pages having related content => search engine
- 9. Web Usage Mining - Pattern Discovery Tasks (Cont.) Classification: the technique to map a data item into one of several predefined classes ◦ Develop profile of users belonging to a particular class or category Association Rules: discover correlations among pages accessed together by a client ◦ Help the restructure of Web site ◦ Page prefetching ◦ Develop e-commerce marketing strategies
- 10. Web Usage Mining - Pattern Discovery Tasks (Cont.) Sequential Patterns: extract frequently occurring intersession patterns such that the presence of a set of items followed by another item in time order ◦ Predict future user visit patterns=>placing ads or recommendations ◦ Page prefeteching Dependency Modeling: determine if there are any significant dependencies among the variables in the Web domain ◦ Predict future Web resource consumption ◦ Develop business strategies to increase sales ◦ Improve navigational convenience of users
- 11. Web Usage Mining - Pattern Analysis Pattern Analysis is the final stage of WUM, which involves the validation and interpretation of the mined pattern Validation: to eliminate the irrelative rules or patterns and to extract the interesting rules or patterns from the output of the pattern discovery process Interpretation: the output of mining algorithms is mainly in mathematic form and not suitable for direct human interpretations
- 12. Web Usage Mining - Pattern Analysis Methodologies and Tools Visualization: help people to understand both real and abstract concepts ◦ WebViz: Web is visualized as a direct graph Query mechanism: allow analysts to extract only relevant and useful patterns by specifying constraints. ◦ WEBMINER On-Line Analytical Processing (OLAP): enable analysts to perform ad hoc analysis of data in multiple dimensions for decision-making ◦ WebLogMiner
- 13. Application Areas for Web Usage Mining Personalized: discover the preference and needs ofindividual Web users in order to provide personalized Web site for certain types of users Impersonalized: examine general user navigation patterns in order to understand how general users use the site ◦ System Improvement ◦ Site Modification ◦ Business Intelligence ◦ Web Characterization
- 14. Future Research Directions Usage Mining on Semantic Web ◦ Help to build semantic Web ◦ With semantic Web, WUM can be improved Multimedia Web Data Mining ◦ Representation, problem solving and learning from Multimedia data is indeed a challenge
- 15. Future Research Directions (Cont.) Analysis of Discovered Patterns ◦ Research on efficient, flexible and powerful analysis tools More Applications ◦ Temporal evolutions of usage behavior ◦ Improving Web services ◦ Detect credit card fraud ◦ Privacy issues
- 16. Conclusion Web usage and data mining to find patterns is a growing area with the growth of Web-based applications Application of web usage data can be used to better understand web usage, and apply this specific knowledge to better serve users Web usage patterns and data mining can be the basis for a great deal of future research
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