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1416 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 Figure 6. Bar diagram comparing the area efficiency metric for different architectures and for window sizes from 3x3 to 19x19 using the parameters of the case study. The lower the bar, the more efficient. REFERENCE [1] Gonzalez, R.C. and R.E. Woods, “Digital Image Processing,” Prentice Hall Press, 2002 [2] B. S. Wu, C. C. Hsieh and C. C. Lee, “A Distance Computer Vision Assisted Yoga Learning System,” Journal of Computers, 11(6): pp.2382-2388, 2011 [3] Z. Wang and X. Sun, “Orthogonal Maximum Margin Projection for Face Recognition,” Journal of Computers, 2(7): pp.377-383, 2012 [4] B. Zhu and W. Jin, “Radar Emitter Signal Recognition Based on EMD and Neural Network,” Journal of Computers, 6(7): pp.1413-1420, 2012 [5] Hecht, V. and K. Ronner, “An Advanced Programmable 2D-convolution Chip for Real Time Image Processing,” IEEE International Sympoisum on Circuits and Systems, pp.1897-1900, 1991 [6] Leblebici, Y., et al., “A Fully Pipelined Programmable Real-time (3×3) Image Filter Based on Capacitive Thresholdlogic gates,” Proceedings of IEEE International Symposium on Circuits and Systems, vol.3, pp. 2072-2075, 1997 [7] Bosi, B., G. Bois, and Y. Savaria, “Reconfigurable Pipelined 2-D Convolvers for Fast Digital Signal Processing,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 7(3): pp. 299-308, 1999 [8] Liang, X., J. Jean, and K. Tomko, “Data Buffering and Allocation in Mapping Generalized Template Matching on Reconfigurable Systems,” The Journal of Supercomputing, 19(1): pp. 77-91, 2001 [9] Nakajima, M., et al., “A 40GOPS 250mw Massively Parallel Processor Based on Matrix Architecture,” IEEE International Solid-State Circuits Conference, pp.1616-1625, 2006 [10] Cardells-Tormo, F. and P.L. Molinet, “Area-efficient 2-D Shift-variant Convolvers for FPGA-based Digital Image Processing,” IEEE Workshop on Signal Processing Systems Design and Implementation, pp. 209-213, 2005 Zhijian Lu is a Ph.D. student in College of Computer Science and Technology of Harbin Engineering University, Harbin, China. His current research interest includes neural network, reconfigurable computing and image processing. Yanxia Wu is Associate Professor in College of Computer Science and Technology of Harbin Engineering University, Harbin, China. Her current research interests include safe compiler, reconfigurable compiler and computer architecture. Zhenhua Guo is a Ph.D. student in College of Computer Science and Technology of Harbin Engineering University, Harbin, China. His current research interest includes reconfigurable computing and embedded system. Guochang Gu is Professor in College of Computer Science and Technology of Harbin Engineering University, Harbin, China. His main research interests include embedded systems and safe compiler. © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1417 AT-Mine: An Efficient Algorithm of Frequent Itemset Mining on Uncertain Dataset Le Wang a,b ,Lin Feng a,b, *, and Mingfei Wu a,b a School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China 116024. b School of Innovation and Experiment, Dalian University of Technology, Liaoning, China 116024. Email: lelewater@gmail.com; fenglin@dlut.edu.cn; merphy.wmf@gmail.com Abstract—Frequent itemset/pattern mining (FIM) over uncertain transaction dataset is a fundamental task in data mining. In this paper, we study the problem of FIM over uncertain datasets. There are two main approaches for FIM: the level-wise approach and the pattern-growth approach. The level-wise approach requires multiple scans of dataset and generates candidate itemsets. The pattern-growth approach requires a large amount of memory and computation time to process tree nodes because the current algorithms for uncertain datasets cannot create a tree as compact as the original FP-Tree. In this paper, we propose an array based tail node tree structure (namely AT-Tree) to maintain transaction itemsets, and a pattern-growth based algorithm named AT-Mine for FIM over uncertain dataset. AT-Tree is created by two scans of dataset and it is as compact as the original FP-Tree. AT-Mine mines frequent itemsets from AT-Tree without additional scan of dataset. We evaluate our algorithm using sparse and dense datasets; the experimental results show that our algorithm has achieved better performance than the state-of-the-art FIM algorithms on uncertain transaction datasets, especially for small minimum expected support number. Index Terms—data mining, frequent itemset, frequent pattern, uncertain dataset I. INTRODUCTION Frequent itemsets mining (FIM) over transaction dataset is an important and common topic in data mining. The algorithm Apriori [1] was first proposed to discover frequent itemsets from market basket data. Since then, FIM algorithms were constantly proposed for various application domains, such as those for complete frequent itemsets [1, 2, 3, 4, 5, 6, 7], for maximal frequent itemsets [8, 9, 10, 11, 12], for closed frequent itemsets [13, 14, 15] and for frequent sequential patterns [16] and high uitlity itemsets [17, 18, 19, 20]. These algorithms concern precise transaction datasets, that is, all items can be described with a certain value. However, many real-world applications generate or require uncertain transaction datasets in which items can only be described with an existential probability. For example, some diseases can Manuscript received January 12, 2013; revised February. 11, 2013; accepted March 1, 2011. This work was supported by National Natural Science Foundation of P.R. China (61173163, 51105052), Program for New Century Excellent Talents in University (NCET-09-0251); and Liaoning Provincial Natural Science Foundation of China (Grant No. 201102037). Corresponding Author: Lin Feng; E-mail: fenglin@dlut.edu.cn. not be definitely diagnosed by a set of symptons - they can only be ascertained as a probability value; the locations of a moving object obtained through RFID or GPS devices are not precise [21, 22]; the shopping habits mined from an e-commerce website are also probability values for predicting what a customer will buy in the future. TABLE I. AN EXAMPLE OF UNCERTAIN DATASET TID Transaction itemset T 1 (a: 0.8), (b: 0.7), (d: 0.9), (f: 0.5) T 2 (c: 0.8), (d: 0.85), (e: 0.4) T 3 (c: 0.85), (d: 0.6), (e: 0.6) T 4 (a: 0.9) , (b: 0.85), (d: 0.65) T 5 (a: 0.95), (b: 0.7), (d: 0.8) , (e: 0.7) T 6 (b: 0.7), (c: 0.65), (f: 0.45) Table 1 shows an example of uncertain transaction dataset, each transaction of which represents that a customer might buy a certain item with a probability. The value associated with each item is called the existential probability of the item. For instance, the first transaction T 1 in Table 1 shows that a customer might purchase “a”, “b”, “d” and “f” with 80%, 70%, 90% and 50% chances in the future respectively. In recent years, FIM over uncertain datasets has become an important topic in data mining [23, 24, 25, 26, 27, 28, 29, 30, 31, 32]. The existing algorithms can be classified into two main categories: the level-wise approach and the pattern-growth approach. The algorithms U-Apriori [31], MBP [28] and IMBP [26] employ the level-wise approach, and all these algorithms generate candidates and require multiple scans of the dataset. The algorithms UH-Mine [30], UFP-Growth [30], and UF-Growth [25, 29] employ the pattern-growth approach. UH-Mine is based on the algorithm H-Mine [5]; UF-Growth is based on the classical algorithm FP- Growth [2] and employs the same method as FP-Growth for mining frequent itemsets on uncertain transaction itemsets. Both UH-Mine and UF-Growth cannot create a tree as compact as FP-Tree for maintaining transaction itemsets, thus they require a large amount of memory and computational time to process tree nodes, especially on © 2013 ACADEMY PUBLISHER doi:10.4304/jcp.8.6.1417-1426
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Call for Papers and Special Issues
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(Contents Continued from Back Cover
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