Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Decision support for real-time telemarketing operations through Bayesian network learning
Decision Support Systems - Special issue: knowledge discovery and its applications to business decision making
Data mining solves tough semiconductor manufacturing problems
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Intelligent Technologies for Managing Fraud and Identity Theft
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Computational Statistics & Data Analysis
Selecting prospects for cross-selling financial products using multivariate credibility
Expert Systems with Applications: An International Journal
Data mining applied to the cognitive rehabilitation of patients with acquired brain injury
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Due to the information technology improvement and the growth of internet, enterprises are able to collect and to store huge amount of data. Using data mining technology to aid the data processing, information retrieval and knowledge generation process has become one of the critical missions to enterprise, so how to use data mining tools properly is user concern. Since not every user completely understand the theory of data mining, choosing the best solution from the functions which data mining tools provides is not easy. If user is not satisfied with the outcome of mining, communication with IT employees to adjust the software costs lots of time. To solve this problem, a selection model of data mining algorithms is proposed. By analyzing the content of business decision and application, user requirements will map to certain data mining category and algorithm. This method makes algorithm selection faster and reasonable to improve the efficiency of applying data mining tools to solve business problems.