07-04-2011, 03:34 PM
PRESENTED BY
Nidhi
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Data Mining Terminologies
• Data
Data are any facts, numbers, or text that can be processed by a computer.
• Information
The patterns, associations, or relationships among all this data can provide information.
• Knowledge
Information can be converted into knowledge about historical patterns and future trends.
• Data Warehouses
Data Warehouse is a repository of information collected from multiple sources, stored under a unified schema, and that usually resides at a single site.
• Association Analysis
Association analysis is the discovery of association rules showing attribute-value conditions that occur frequently together in a given set of data.
• Data Mining
• It is the extraction of hidden predictive information from large databases.
• Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions.
Application Area:
Web Usage Mining
• Web Usage 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 Usage Mining Processes
• Preprocessing: conversion of the raw data into the data abstraction (users, sessions, episodes, click streams, and page views) 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 Data Mining Process
• Plan of proposed work
• The web data are very complex due to their large size and sequential nature.
• In the past, researchers have proposed different methods to predict what pages will be visited next based on their present visit patterns.
• I am extending this work to discover patterns that can predict when these web page accesses will occur.
Plan to proceed further
Data mining efforts associated with web, called Web Mining can be divided into three categories
• Web Content Mining- extracting knowledge from the content of the Web
• Web Structure Mining- discovering the model underlying the page link structures of the Web
• Web Usage Mining- discovering user’s navigation pattern and predicting user’s behavior
What web data is being mined?
• Content – data from Web documents – text & graphics
• Structure – data from Web Structure – HTML or XML tags
• Usage – data from Web log data – IP addresses, date & time access
• User Profile – data that is user specific – registration and customer profile
Web Usage Mining -
Pattern Discovery Tasks
• Statistical Analysis
• Clustering
• Classification
• Association Rules
• Sequential Patterns
• Dependency Modeling
• 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
• 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
• Sequential Patterns: extract frequently occurring inter-session patterns such that the presence of a set of items s followed by another item in time order
– Predict future user visit patterns=>placing ads or recommendations
– Page prefetching
• 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: such as graphing patterns help people to understand both real and abstract concepts
• Query mechanism: allow analysts to extract only relevant and useful patterns by specifying constraints.
• On-Line Analytical Processing (OLAP): enable analysts to perform ad hoc analysis of data in multiple dimensions for decision-making
Objectives of Web Usage Mining
• System Improvement
• Site Modification
• Business Intelligence
• Usage Characterization
System Improvement
• Performance and other service quality attributes are crucial to user satisfaction and high quality performance of a web application is expected
• Web usage mining of patterns provides a key to understanding Web traffic behavior, which can be used to deal with policies on web caching, network transmission, load balancing, or data distribution
• Web usage and data mining is also useful for detecting intrusion, fraud, and attempted break-ins to the system
Site Modification
• This application of web usage patterns involves the attractiveness of a Web site, in terms of content and structure
• Web usage patterns or mining can provide detailed feedback on user behavior which can lead the Web site designer to information on which to base redesign decisions
Business Intelligence
• Information on how customers are using a Web site is critical information for marketers of e-commerce businesses
Customer relationship life cycle:
– Customer attraction
– Customer retention
– Cross sales
– Customer departure
• Can provide information on products bought and advertisement click-through rates
– Usage Characterization
• Mining of web usage patterns can help in the study of how browsers are used and the user’s interaction with a browser interface
• Usage characterization can also look into navigational strategy when browsing a particular site
• Web usage mining focuses on techniques that could predict user behavior while the user interacts with the Web
Review of Literature Survey:
Web SIFT Example
• Web Site Information Filter System (WebSIFT) is a Web usage mining framework, that uses the content and structure information from a Web site, and identifies the interesting results from mining usage data
• Input of the mining process: server logs (access, referrer, and agent), HTML files, optional data
• Prototypical Web usage mining system
• Review of Literature work contd…
Abstract-One can discover patterns that predict the users’ future requests based on their current behavior to explore the tradeoff between prediction accuracy and data mining time.
Department of Computer Science, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong, China, (qyang,whui)@cs.ust.hk.
• Example Web Log
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
