The improved on-line analysis mining for multi-dimension data based on association rules

Authors:
Yang Bo;Zheng Yongqing
Affiliations:
College of Computer Science and Technology, Shandong University, Shandong, China;College of Computer Science and Technology, Shandong University, Shandong, China
Venue:
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Year:
2010

Citing 5
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining

IEEE Transactions on Knowledge and Data Engineering
Mining Multi-Dimensional Constrained Gradients in Data Cubes

Proceedings of the 27th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the paper, we discussed the characteristics of data mining on association rules for multi-dimension data. Then through the multi-dimension data attributes analysis and OLAP operations, we integrate the OLAP and data mining based on their advantages to one method which is called On-Line Analysis Mining (OLAM) [1]. Based on OLAM, an algorithm for multi-dimension data on association rules has been reformed. It can improve the efficiency and flexible of rules searching. Finally, our performance has proved the algorithm.