Volume 9 Number 1 (Jan. 2014)
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JSW 2014 Vol.9(1): 44-56 ISSN: 1796-217X
doi: 10.4304/jsw.9.1.44-56

UDS-FIM: An Efficient Algorithm of Frequent Itemsets Mining over Uncertain Transaction Data Streams

Le Wang1, 2, 3 Lin Feng2, 3, and Mingfei Wu2, 3
1College of Information Engineering, Ningbo Dahongying University, Ningbo, Zhejiang, China 315175
2School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China 116024
3School 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.

Index Terms—frequent itemset, frequent pattern, uncertain dataset, data streams, data mining

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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.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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