By Xudong Luo, Jeffrey Xu Yu, Zhi Li
This publication 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 average papers and 10 workshop papers awarded during this quantity have been conscientiously reviewed and chosen from ninety submissions. They care for the subsequent issues: info mining, social community and social media, suggest platforms, database, dimensionality aid, increase laptop studying options, category, great facts and functions, clustering equipment, desktop studying, and information 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
These assumptions often do not hold in real applications. For example, consider a database of customer transactions containing information about the quantities of items in each transaction and the unit proﬁt of each item. FIM algorithms would discard this information and may thus discover many frequent itemsets generating a low proﬁt and fail to discover less frequent itemsets that generate a high proﬁt. g. unit proﬁt). e. itemsets generating a high proﬁt. HUIM has a wide range of applications such as website click stream analysis, cross-marketing in retail stores and biomedical applications [3,9,12].
Mining High Utility Episodes in Complex Event Sequences. In: Proceedings of ACM SIG KDD 2013, pp. 536–544 (2013) 15. : USpan: An Eﬃcient Algorithm for Mining High Utility Sequential Patterns. In: Proceedings of ACM SIG KDD 2012, pp. 660–668 (2012) 16. : Eﬃciently Mining Top-K High Utility Sequential Patterns. In: Proceedings of ICDM 2013, pp. 1259–1264 (2013) Novel Concise Representations of High Utility Itemsets Using Generator Patterns Philippe Fournier-Viger1, Cheng-Wei Wu2 , and Vincent S.
Journal of Computer Science and Technology 15(6), 619–624 (2000) 9. : A Matrix Algorithm for Mining Association Rules. -B. ) ICIC 2005. LNCS, vol. 3644, pp. 370–379. Springer, Heidelberg (2005) 10. : A Fast Algorithm for Mining association Rules Based on Boolean Matrix. In: 2008 International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China, pp. 1–3 (2008) 11. : An improved Apriori algorithm based on the matix. In: 2008 International Seminar on Future BioMedical Information Engineering, Wuhan, China, pp.
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