Download Advances in Knowledge Discovery and Data Mining: 17th by Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent PDF

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By Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed lawsuits of the seventeenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the complete of ninety eight papers provided in those court cases was once rigorously reviewed and chosen from 363 submissions. They hide the overall fields of information mining and KDD commonly, together with trend mining, category, graph mining, functions, computer studying, function choice and dimensionality aid, a number of info assets mining, social networks, clustering, textual content mining, textual content class, imbalanced info, privacy-preserving information mining, advice, multimedia info mining, movement info mining, information preprocessing and representation.

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Extra info for Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I

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PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining 23 Table 2. 32% 881,010 872,062 Fig. 4. 2 Runtime Recall that UH-Mine was shown to outperform the UFP-growth [2,15]. So, we also compared our PUF-growth with UH-Mine. Figs. 4(a)–(c) show that PUF-growth took shorter runtime than UH-Mine for datasets u100k10L 50 60, u100k10L 10 100 and mushroom 50 60. The primary reason is that, even though the UH-Mine finds the exact set of frequent patterns when mining an extension of X, it may suffer from the high computation cost of calculating the expected support of X on-the-fly for all transactions containing X.

Next, we observe the qualities found for the same prototype, for smaller values of ρ, in decreasing order. If the optimal quality found for such a smaller reference group, qoptimal (ρ), is lower than the threshold, we skip the estimation of the DFD and the calculation of the p-value, because we know the p-value will be lower. For smaller values of ρ that have a higher optimal quality, we compute the DFD. We also obtain the cumulative distribution function of the DFD, ΦDF D , and Discovering Local Subgroups, with an Application to Fraud Detection 9 the inverse cumulative distribution function, Φ−1 DF D .

When handling uncertain data, UF-growth and UFP-growth are examples of well-known mining algorithms, which use the UF-tree and the UFP-tree respectively. However, these trees can be large, and thus degrade the mining performance. In this paper, we propose (i) a more compact tree structure to capture uncertain data and (ii) an algorithm for mining all frequent patterns from the tree. Experimental results show that (i) our tree is usually more compact than the UF-tree or UFP-tree, (ii) our tree can be as compact as the FP-tree, and (iii) our mining algorithm finds frequent patterns efficiently.

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