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

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

ISBN-10: 3642374522

ISBN-13: 9783642374524

ISBN-10: 3642374530

ISBN-13: 9783642374531

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 full of ninety eight papers offered in those lawsuits was once conscientiously reviewed and chosen from 363 submissions. They conceal the final fields of knowledge mining and KDD broadly, together with development mining, type, graph mining, functions, computer studying, characteristic choice and dimensionality aid, a number of details resources mining, social networks, clustering, textual content mining, textual content type, imbalanced information, privacy-preserving information mining, suggestion, multimedia info mining, circulation info mining, info preprocessing and representation.

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Additional 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

Example text

The above lemma allows us to prune the constructed PUF-tree further by removing any item having a total item cap (in the I-list ) less than minsup. ) Hence, we can remove item d from the PUF-tree in Fig. 2(c) because expSupCap (d) < minsup. This results in a more compact PUF-tree, as shown in Fig. 2(d). This tree-pruning technique can save the mining time as it skips those items. Let F (tj ) be the set of frequent items in transaction tj . , total item cap) of an item x for all transactions that pass through or end at x.

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. 1–12 (2000) 8. : Efficient dynamic mining of constrained frequent sets. ACM TODS 28(4), 337–389 (2003) 9. : Mining uncertain data. WIREs Data Mining and Knowledge Discovery 1(4), 316–329 (2011) 10.

An algorithm for multi-relational discovery of subgroups. M. ) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997) PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data Carson Kai-Sang Leung and Syed Khairuzzaman Tanbeer Dept. ca Abstract. Many existing algorithms mine frequent patterns from traditional databases of precise data. However, there are situations in which data are uncertain. In recent years, researchers have paid attention to frequent pattern mining from uncertain data.

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Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I by Rob M. Konijn, Wouter Duivesteijn (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)


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