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 ebook constitutes the lawsuits of the tenth foreign convention on complicated facts Mining and functions, 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 subsequent issues: facts mining, social community and social media, suggest platforms, database, dimensionality relief, boost laptop studying recommendations, category, vast info and purposes, clustering tools, computer 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

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Improved appriori mining frequent items algorithm. Application Research of computation 29(2), 475–477 (2012) (in Chinese) 22. : New optimization association rule algorithm based on array. Science Technology and Engineering 8(21), 5846–5849 (2008) (in Chinese) 23. : An improved Appriori algorithm based on compression matrix approach. Microelectronics and Computer 29(6), 28–32 (2010) (in Chinese) FHN: Efficient Mining of High-Utility Itemsets with Negative Unit Profits Philippe Fournier-Viger Dept.

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.

The rutil element of a tuple is defined as i∈Ttid ∧i x∀x∈X u(i, Ttid ). Example 7. Assume that is the alphabetical order and that the external utility of item a is 5 rather than -5. The utility-list of {a} is {(T1 , 5, 3), (T2 , 10, 17), (T3 , 5, 25)}. The utility-list of {d} is {(T1 , 2, 0), (T3 , 12, 8), (T4 , 6, 3)}. The utilitylist of {a, d} is {(T1 , 7, 0), (T3 , 17, 8)}. FHM discovers HUIs by performing a single database scan to create utilitylists of patterns containing single items. Then, longer patterns are obtained by performing the join operation of utility-lists of shorter patterns.

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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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