31-07-2012, 12:14 PM
DATA MINING AND WAREHOUSING
DATA MINING.ppt (Size: 357 KB / Downloads: 68)
Data Mining Applications
Data mining is an interdisciplinary field with wide and diverse applications
There exist nontrivial gaps between data mining principles and domain-specific applications
Some application domains
Financial data analysis
Retail industry
Telecommunication industry
Biological data analysis
Data Mining for Financial Data Analysis
Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high quality
Typical Cases of DM in Financial data analysis
Design and construction of data warehouses for multidimensional data analysis and data mining
View the debt and revenue changes by month, by region, by sector, and by other factors
Access statistical information such as max, min, total, average, trend, etc.
Loan payment prediction/consumer credit policy analysis
feature selection and attribute relevance ranking
Loan payment performance
Consumer credit rating
Data Mining for the Retail Industry
Retail industry: huge amounts of data on sales, customer shopping history, etc.
Applications of retail data mining
Identify customer buying behaviors
Discover customer shopping patterns and trends
Improve the quality of customer service
Achieve better customer retention and satisfaction
Enhance goods consumption ratios
Design more effective goods transportation and distribution policies
Data Mining for the Telecommunication Industry
Fraudulent pattern analysis and the identification of unusual patterns
Identify potentially fraudulent users and their atypical usage patterns
Detect attempts to gain fraudulent entry to customer accounts
Discover unusual patterns which may need special attention
Multidimensional association and sequential pattern analysis
Find usage patterns for a set of communication services by customer group, by month, etc.
Promote the sales of specific services
Improve the availability of particular services in a region
Social Impacts of Data Mining
For most of us, data mining is part of our daily lives, although we may often be unaware of its presence
Data mining can offer the individual many benefits by improving customer service and satisfaction, and lifestyle, in general
However, it also has serious implications regarding one’s right to privacy and data security
Summary
Domain-specific applications include biomedicine (DNA), finance, retail and telecommunication data mining
There exist some data mining systems and it is important to know their power and limitations
Visual data mining include data visualization, mining result visualization, mining process visualization and interactive visual mining
There are many other scientific and statistical data mining methods developed but not covered in this book
Also, it is important to study theoretical foundations of data mining
Intelligent query answering can be integrated with mining
It is important to watch privacy and security issues in data mining
DATA MINING.ppt (Size: 357 KB / Downloads: 68)
Data Mining Applications
Data mining is an interdisciplinary field with wide and diverse applications
There exist nontrivial gaps between data mining principles and domain-specific applications
Some application domains
Financial data analysis
Retail industry
Telecommunication industry
Biological data analysis
Data Mining for Financial Data Analysis
Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high quality
Typical Cases of DM in Financial data analysis
Design and construction of data warehouses for multidimensional data analysis and data mining
View the debt and revenue changes by month, by region, by sector, and by other factors
Access statistical information such as max, min, total, average, trend, etc.
Loan payment prediction/consumer credit policy analysis
feature selection and attribute relevance ranking
Loan payment performance
Consumer credit rating
Data Mining for the Retail Industry
Retail industry: huge amounts of data on sales, customer shopping history, etc.
Applications of retail data mining
Identify customer buying behaviors
Discover customer shopping patterns and trends
Improve the quality of customer service
Achieve better customer retention and satisfaction
Enhance goods consumption ratios
Design more effective goods transportation and distribution policies
Data Mining for the Telecommunication Industry
Fraudulent pattern analysis and the identification of unusual patterns
Identify potentially fraudulent users and their atypical usage patterns
Detect attempts to gain fraudulent entry to customer accounts
Discover unusual patterns which may need special attention
Multidimensional association and sequential pattern analysis
Find usage patterns for a set of communication services by customer group, by month, etc.
Promote the sales of specific services
Improve the availability of particular services in a region
Social Impacts of Data Mining
For most of us, data mining is part of our daily lives, although we may often be unaware of its presence
Data mining can offer the individual many benefits by improving customer service and satisfaction, and lifestyle, in general
However, it also has serious implications regarding one’s right to privacy and data security
Summary
Domain-specific applications include biomedicine (DNA), finance, retail and telecommunication data mining
There exist some data mining systems and it is important to know their power and limitations
Visual data mining include data visualization, mining result visualization, mining process visualization and interactive visual mining
There are many other scientific and statistical data mining methods developed but not covered in this book
Also, it is important to study theoretical foundations of data mining
Intelligent query answering can be integrated with mining
It is important to watch privacy and security issues in data mining