Data Mining

Get Advanced Data Mining and Applications: 10th International PDF

By Xudong Luo, Jeffrey Xu Yu, Zhi Li

ISBN-10: 3319147161

ISBN-13: 9783319147161

ISBN-10: 331914717X

ISBN-13: 9783319147178

This booklet constitutes the complaints of the tenth overseas convention on complicated info Mining and functions, ADMA 2014, held in Guilin, China in the course of December 2014. The forty eight normal papers and 10 workshop papers offered during this quantity have been rigorously reviewed and chosen from ninety submissions. They care for the subsequent subject matters: facts mining, social community and social media, suggest structures, database, dimensionality relief, strengthen laptop studying suggestions, category, great info and purposes, clustering equipment, computing device studying, and information mining and database.

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Read Online or Download Advanced Data Mining and Applications: 10th International Conference, ADMA 2014, Guilin, China, December 19-21, 2014. Proceedings PDF

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

Sample text

X. Yu, and Z. ): ADMA 2014, LNAI 8933, pp. 16–29, 2014. c Springer International Publishing Switzerland 2014 FHN: Efficient Mining of High-Utility Itemsets with Negative Unit Profits 17 has also inspired several important data mining tasks such as high-utility sequential pattern mining [15,16], high-utility episode mining [14] and high-utility stream mining [11]. The problem of HUIM is widely recognized as more difficult than the problem of FIM. In FIM, the downward-closure property states that the support of an itemset is anti-monotonic, that is the supersets of an infrequent itemset are infrequent and subsets of a frequent itemset are frequent.

G. proprietary printer cartridges). It was demonstrated that if classical HUIM algorithms are applied on databases containing items with negative unit profits, they can generate an incomplete set of HUIs [1]. The reason is that these algorithms over-estimate the utility of itemsets to prune the search space. But, when items with negative unit profits are considered, these estimations may become underestimations, and thus HUIs may be pruned. The state-of-the-art algorithm for mining HUIs while considering negative unit profits is HUINIV-Mine [1].

Data Eng. 21(12), 1708–1721 (2009) 4. : FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning. W. ) ISMIS 2014. LNCS, vol. 8502, pp. 83–92. Springer, Heidelberg (2014) 5. : Fast vertical mining of sequential patterns using co-occurrence information. -Y. ) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 40–52. Springer, Heidelberg (2014) 6. : VMSP: Efficient Vertical Mining of Maximal Sequential Patterns. , van Beek, P. ) Canadian AI. LNCS, vol. 8436, pp. 83–94. Springer, Heidelberg (2014) 7.

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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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