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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