Download Advanced Data Mining and Applications: 10th International by Xudong Luo, Jeffrey Xu Yu, Zhi Li PDF

By Xudong Luo, Jeffrey Xu Yu, Zhi Li

This e-book constitutes the lawsuits of the tenth overseas convention on complex info Mining and purposes, ADMA 2014, held in Guilin, China in the course of December 2014. The forty eight typical papers and 10 workshop papers awarded during this quantity have been conscientiously reviewed and chosen from ninety submissions. They take care of the next issues: info mining, social community and social media, suggest structures, database, dimensionality relief, enhance computer studying recommendations, class, significant information and functions, clustering tools, computer studying, and knowledge mining and database.

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

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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. IEEE Trans. Knowl. Data Eng. 25(8), 1772–1786 (2013) 13. : Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets.

Property 4 (Downward closure for generator patterns). An itemset X is not a generator pattern if there exists a strict subset of X that is not a generator [11]. 3 Integrating the Concept of Generator in HUIM To understand how the concept of generator can be applied to HUIM, consider the equivalence classes shown in Fig. 2 for the running example. Each equivalence class is represented as a Hasse diagram inside a rectangle and is labelled with the supporting transactions and support of its itemsets.

Results show that FHN is up to 500 times faster and can use up to 250 times less memory than HUINIV-Mine, and was shown to perform very well on dense datasets. philippe-fournier-viger/spmf/. For future work, we are interested in exploring other interesting problems involving utility mining in itemset mining and sequential pattern mining [5,6]. Acknowledgement. This work is financed by a National Science and Engineering Research Council (NSERC) of Canada research grant. FHN: Efficient Mining of High-Utility Itemsets with Negative Unit Profits 29 References 1.

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