web usage pattern
TRANSCRIPT
Web Usage Pattern
SHAH RUSHABH R CE-111SHREYANSH R KEJRIWAL CE-113
Outline Brief overview of Web mining Web usage mining Application areas of Web usage
mining Future research directions
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.
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
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
Web Usage Mining Processes(Cont.)
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.
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 pageshaving related content => search engine
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
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
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
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
Application Areas forWeb 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
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
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
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 tobetter understand web usage, and apply thisspecific 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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