Forecasting with neural networks
Information and Management
Improving the accuracy of Institute for Scientific Information's journal impact factors
Journal of the American Society for Information Science
A probabilistic neural network approach to jet engine fault diagnosis
IEA/AIE '95 Proceedings of the 8th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Applications of machine learning and rule induction
Communications of the ACM
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Communications of the ACM
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Decision Support and Business Intelligence Systems (8th Edition)
Decision Support and Business Intelligence Systems (8th Edition)
Customer Relationship Management: Getting It Right!
Customer Relationship Management: Getting It Right!
Accelerating customer relationships: using crm and relationship technologies™
Accelerating customer relationships: using crm and relationship technologies™
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Knowledge management vs. data mining: Research trend, forecast and citation approach
Expert Systems with Applications: An International Journal
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There are few comprehensive studies and categorization schemes to discuss the characteristics for both data mining and customer relationship management (CRM) although they have already become more important recently. Using a bibliometric approach, this paper analyzes data mining and CRM research trends from 1989 to 2009 by locating headings "data mining" and "customer relationship management" or "CRM" in topics in the SSCI database. The bibliometric analytical technique was used to examine these two topics in SSCI journals from 1989 to 2009, we found 1181 articles with data mining and 1145 articles with CRM. This paper implemented and classified data mining and CRM articles using the following eight categories--publication year, citation, country/territory, document type, institute name, language, source title and subject area--for different distribution status in order to explore the differences and how data mining and CRM technologies have developed in this period and to analyze data mining and CRM technology tendencies under the above result. Also, the paper performs the K---S test to check whether the analysis follows Lotka's law. The research findings can be extended to investigate author productivity by analyzing variables such as chronological and academic age, number and frequency of previous publications, access to research grants, job status, etc. In such a way characteristics of high, medium and low publishing activity of authors can be identified. Besides, these findings will also help to judge scientific research trends and understand the scale of development of research in data mining and CRM through comparing the increases of the article author. Based on the above information, governments and enterprises may infer collective tendencies and demands for scientific researcher in data mining and CRM to formulate appropriate training strategies and policies in the future. This analysis provides a roadmap for future research, abstracts technology trends and facilitates knowledge accumulations so that data mining and CRM researchers can save some time since core knowledge will be concentrated in core categories. This implies that the phenomenon "success breeds success" is more common in higher quality publications.