Mining

Data Mining and Big Data: First International Conference, by Ying Tan, Yuhui Shi

By Ying Tan, Yuhui Shi

The LNCS quantity LNCS 9714 constitutes the refereed complaints of the foreign convention on info Mining and massive information, DMBD 2016, held in Bali, Indonesia, in June 2016.
The fifty seven papers provided during this quantity have been rigorously reviewed and chosen from a hundred and fifteen submissions. The subject of DMBD 2016 is "Serving existence with information Science". facts mining refers back to the job of dealing with massive information units to seem for appropriate or pertinent information.The papers are prepared in 10 cohesive sections overlaying all significant subject matters of the study and improvement of knowledge mining and large info and one Workshop on Computational points of development acceptance and laptop Vision.

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Extra info for Data Mining and Big Data: First International Conference, DMBD 2016, Bali, Indonesia, June 25-30, 2016. Proceedings

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Appl. 41(5), 2239–2249 (2014) 7. : Knowledge discovery in medicine: current issue and future trend. Exp. Syst. Appl. 41(9), 4434–4463 (2014) 8. : Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012) 9. : Educational data mining: a survey and a data mining-based analysis of recent works. Exp. Syst. Appl. 41(4), 1432–1462 (2014) 10. : Data mining for internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 77–97 (2014) 11. : Web Mining technologies for the e-Commerce solutions in the social networks systems.

At each iteration t, the algorithm tests the predictions of the newly generated weak hypothesis ht and uses their results to update the weight distribution Dt , then the new weight distribution Dt+1 along with the training samples will pass to the weak learner to build a new weak hypothesis ht+1 . , hT in a linear weighting way. MH algorithm are showed as follows. MH. MH. MP-Boost basically concentrates on the form of weak hypotheses and how they are generated. MH is as follows: Algorithm 1. , m.

E-Manage. e-Lear. (IJEEEE) 4(1), 23–27 (2014) 21. : Intelligent information system to support decision-making based on unstructured web data. ICIC Expr. Lett. cn Abstract. AdaBoost is an ensemble method, which is considered to be one of the most influential algorithms for multi-label classification. It has been successfully applied to diverse domains for its tremendous simplicity and accurate prediction. To choose the weak hypotheses, AdaBoost has to examine the whole features individually, which will dramatically increase the computational time of classification, especially for large scale datasets.

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