New PDF release: Advanced Data Mining and Applications: 10th International

By Xudong Luo, Jeffrey Xu Yu, Zhi Li

ISBN-10: 3319147161

ISBN-13: 9783319147161

ISBN-10: 331914717X

ISBN-13: 9783319147178

This e-book constitutes the court cases of the tenth foreign convention on complex information Mining and purposes, ADMA 2014, held in Guilin, China in the course of December 2014. The forty eight standard papers and 10 workshop papers offered during this quantity have been rigorously reviewed and chosen from ninety submissions. They take care of the next themes: information mining, social community and social media, suggest platforms, database, dimensionality relief, boost computing device studying innovations, class, immense information and functions, clustering equipment, laptop studying, and knowledge mining and database.

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Additional resources for Advanced Data Mining and Applications: 10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014. Proceedings

Sample text

Fig. 1 (right) indicates that the external utility of a, b, e are respectively p(a) = 5, p(c) = 1 and p(e) = 3. Definition 1 (Utility of an itemset). The utility of an item i in a transaction Tc is denoted as u(i, Tc ) and defined as p(i) × q(i, Tc ). An itemset is a set of items. The utility of an itemset X in a transaction Tc is defined as u(X, Tc ) = i∈X u(i, Tc ). The set of transactions containing X is denoted as g(X). The utility of X in a database is defined as u(X) = Tc ∈g(X) u(X, Tc ). Example 1.

Let the term ”positive items” and ”negative items” denote items respectively having positive and negative external utility values. To be able to transform the algorithm described in the previous subsection into an algorithm that outputs all HUIs when both negative and positive items are used, we first make a few novel and very important observations that were not done or used in HUINIV-Mine. It is well-known in HUI mining that appending a positive item z to an itemset X will produce an itemset X ∪ {z} that may have a utility that is higher, equal or less than X [10].

Mining High Utility Itemsets without Candidate Generation. In: Proceedings of CIKM 2012, pp. 55–64 (2012) 10. : A two-phase algorithm for fast discovery of high utility itemsets. , Liu, H. ) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005) 11. : A One-Phase Method for Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments. , Wu, X. ) IEA/AIE 2012. LNCS, vol. 7345, pp. 616–626. Springer, Heidelberg (2012) 12. : Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases.

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Advanced Data Mining and Applications: 10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014. Proceedings by Xudong Luo, Jeffrey Xu Yu, Zhi Li

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