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

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 information Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the complete of ninety eight papers offered in those court cases was once conscientiously reviewed and chosen from 363 submissions. They conceal the final fields of information mining and KDD greatly, together with trend mining, category, graph mining, functions, desktop studying, function choice and dimensionality relief, a number of details resources mining, social networks, clustering, textual content mining, textual content type, imbalanced facts, privacy-preserving info mining, advice, multimedia information mining, flow info mining, info 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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119–127 (2009) 4. : Approximation of frequentness probability of itemsets in uncertain data. In: IEEE ICDM 2010, pp. 749–754 (2010) 5. : Efficient pattern mining of uncertain data with sampling. , Pudi, V. ) PAKDD 2010, Part I. LNCS (LNAI), vol. 6118, pp. 480–487. Springer, Heidelberg (2010) 6. : Mining frequent itemsets from uncertain data. , Yang, Q. ) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 47–58. Springer, Heidelberg (2007) 7. : Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp.

Tree paths are shared if the nodes on these paths share the same item, existential probability -value. In general, when dealing with uncertain data, it is not uncommon that the existential probability values of the same item vary from one transaction to another. As such, the resulting UF-tree may not be as compact as the FP-tree. See Fig. 5. The UF-tree contains four nodes for item a with different probability values as children of the root. Efficiency of the corresponding UF-growth algorithm, which finds all and only those frequent patterns, partially relies on the compactness of the UF-tree.

The node counts between PUF-trees and UF-trees, as presented in Table 2, show that PUF-trees were more compact than UF-trees for both sparse and dense #nodes in PUF-tree datasets and for high and low minsup thresholds. 68% of the 121205 nodes in the UF-tree). , u100k10L 10 100) because the PUF-tree is more likely to share paths for common prefixes in dense datasets. In contrast, the UF-tree contains a distinct tree path for each distinct item, existential probability pair, and thus not as compact as the PUF-tree.

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