doi: 10.4304/jsw.9.1.44-56
UDS-FIM: An Efficient Algorithm of Frequent Itemsets Mining over Uncertain Transaction Data Streams
- 1 College of Information Engineering, Ningbo Dahongying University, Ningbo, Zhejiang, China 315175
- 2 School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China 116024
- 3 School of Innovation and Experiment, Dalian University of Technology, Liaoning, China 116024
Abstract
In this paper, we study the problem of finding frequent itemsets from uncertain data streams. To the best of our knowledge, the existing algorithms cannot compress transaction itemsets to a tree as compact as the classical FPTree, thus they need much time and memory space to process the tree. To address this issue, we propose an algorithm UDS-FIM and a tree structure UDS-Tree. Firstly, UDS-FIM maintains probability values of each transactions to an array; secondly, compresses each transaction to a UDS-Tree in the same manner as an FP-Tree (so it is as compact as an FP-Tree) and maintains index of probability values of each transaction in the array to the corresponding tail-nodes; lastly, it mines frequent itemsets from the UDSTree without additional scan of transactions. The experimental results show that UDS-FIM has achieved a good performance under different experimental conditions in terms of runtime and memory consumption.
Keywords
- frequent itemset
- frequent pattern
- unce
How to Cite
Le Wang, Lin Feng, and Mingfei Wu, "UDS-FIM: An Efficient Algorithm of Frequent Itemsets Mining over Uncertain Transaction Data Streams," Journal of Software, vol. 9, no. 1, pp. 44-56, 2014. https://doi.org/ 10.4304/jsw.9.1.44-56
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).