원문정보
초록
영어
Mining association rules is an important research direction in the field of data mining. Related studies have proposed many used to efficiently find large-scale database association rules algorithm, but the research on maintenance problem of association rules is less. Especially many transaction database is always in constant updates. Increase or decrease occurs when the database or dataset minimum support after the change, how to maintain the association rules have been, it got the attention of many researchers. Based on IFP-Growth increment of association rules mining model and to modify the FP-tree, put forward the suitable for transaction data and support the tree model of change, at the same time under different conditions is given incremental association rules mining algorithm, and reduce the frequency of the original dataset range query and query, and in a case of massive dataset multi-level tree structure decomposition, dynamic allocation rule tree branches, ensure load balancing, improve operation efficiency.
목차
1. Introduction
2. VSIFP-Growth
2.1 Problem Model on the VSIFP-Tree
2.2 Algorithm Description of VSIFP-Growth
2.3 An Example of VSIFP-Growth
3. Parallel Computing for Large-Scale Dataset
3.1 PVSIFP-Growth Algorithm Description Based on MapReduce
3.2 PVSIFP-Growth Algorithm Modelling Procedure Based on Mapreduce
4 Experimental Results and Analysis
4.1 The Experimental Data and the Environment
4.2 Incremental Calculation Performance Test of the PVSIFP-Growth Algorithm
5. Conclusion
Acknowledgement
References