Data mining for risk analysis and targeted marketing

1998 ◽  
pp. 158-169
Author(s):  
G. Jha ◽  
S.C. Hui
Keyword(s):  
Data Mining ◽  
Risk Analysis ◽  
Targeted Marketing

THE RESEARCH TREND OF INFORMATION TECHNOLOGY AND DECISION MAKING IN 2009

2010 ◽  
Vol 09 (01) ◽  
pp. 1-8 ◽  
Author(s):  
YONG SHI
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Risk Analysis ◽  
Multiple Criteria Decision Making ◽  
Advisory Board ◽  
Research Trend ◽  
Research Topics

The research topics of the 39 papers published in the International Journal of Information Technology and Decision Making (IT&DM) in 2009 can be classified into three major directions: decision support, multiple criteria decision making, and data mining and risk analysis. The Editor-in-Chief, on behalf of the editorial board and advisory board, highlights the key ideas of these contributions. The seven papers in first issue of 2010 IT&DM are also introduced.

A Framework on Data Mining on Uncertain Data with Related Research Issues in Service Industry

Data Mining ◽  
2013 ◽  
pp. 515-529
Author(s):  
Edward Hung
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Service Industry ◽  
Uncertain Data ◽  
Research Work ◽  
Real Life ◽  
Mean Value ◽  
Customer Relationship ◽  
Statistical Classifiers ◽  
Targeted Marketing

There has been a large amount of research work done on mining on relational databases that store data in exact values. However, in many real-life applications such as those commonly used in service industry, the raw data are usually uncertain when they are collected or produced. Sources of uncertain data include readings from sensors (such as RFID tagged in products in retail stores), classification results (e.g., identities of products or customers) of image processing using statistical classifiers, results from predictive programs used for stock market or targeted marketing as well as predictive churn model in customer relationship management. However, since traditional databases only store exact values, uncertain data are usually transformed into exact data by, for example, taking the mean value (for quantitative attributes) or by taking the value with the highest frequency or possibility. The shortcomings are obvious: (1) by approximating the uncertain source data values, the results from the mining tasks will also be approximate and may be wrong; (2) useful probabilistic information may be omitted from the results. Research on probabilistic databases began in 1980s. While there has been a great deal of work on supporting uncertainty in databases, there is increasing work on mining on such uncertain data. By classifying uncertain data into different categories, a framework is proposed to develop different probabilistic data mining techniques that can be applied directly on uncertain data in order to produce results that preserve the accuracy. In this chapter, we introduce the framework with a scheme to categorize uncertain data with different properties. We also propose a variety of definitions and approaches for different mining tasks on uncertain data with different properties. The advances in data mining application in this aspect are expected to improve the quality of services provided in various service industries.

Data Mining for Targeted Marketing

2004 ◽  
pp. 109-131 ◽  
Author(s):  
Ning Zhong ◽  
Yiyu Yao ◽  
Chunnian Liu ◽  
Jiajin Huang ◽  
Chuangxin Ou
Keyword(s):  
Data Mining ◽  
Targeted Marketing

S33.2: The use of data mining methods in the risk analysis of food borne diseases

2004 ◽  
Vol 46 (S1) ◽  
pp. 70-70
Author(s):  
Sabine Glaser ◽  
Susanne Menzler ◽  
Dirk Werber ◽  
Andrea Ammon ◽  
Lothar Kreienbrock
Keyword(s):  
Data Mining ◽  
Risk Analysis ◽  
Use Of Data ◽  
Food Borne ◽  
Mining Methods
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