By Petra Perner

This ebook constitutes the refereed court cases of the sixteenth commercial convention on Advances in info Mining, ICDM 2016, held in big apple, manhattan, united states, in July 2016.

The 33 revised complete papers offered have been rigorously reviewed and chosen from a hundred submissions. the subjects variety from theoretical elements of information mining to purposes of information mining, corresponding to in multimedia info, in advertising, in drugs, and in procedure regulate, undefined, and society.

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Read or Download Advances in Data Mining. Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016. Proceedings PDF

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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016. Proceedings

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LNCS, vol. 8502, pp. 83–92. Springer, Heidelberg (2014) 6. SPMF: an open-source data mining library. philippe-fournier-viger. com/spmf/ 7. : Effective utility mining with the measure of average utility. Expert Syst. Appl. 38(7), 8259–8265 (2011) 8. : Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: ACM International Conference on Extending Database Technology, pp. 11–20 (2011) 9. : Mining association rules with multiple minimum supports.

Classification methods consisting of four algorithms: Naive Bayes, ID3, CART, LAD tree were used to build faculty performance classification model based on evaluation factors. These factors include: teacher name, speed of delivery, content arrangement, presentation, communication, knowledge, content delivery, explanation power, doubts clearing, discussion of problems, overall completion of course and regularity, students attendance, and result. The result obtained shows that Naïve Bayes algorithm has the highest accuracy.

Do et al. Finding the Relationship Among Evaluation Factors Apriori algorithm was used to find the relationship among the evaluation factors in [11], including: subject knowledge, teaching with new aids, motivating self and students, communication skills, class control, punctuality and regularity, knowledge beyond syllabus, and aggregate. 3 Adjusting Faculty Performance Based on Clustering Evaluation Forms Some algorithms were applied to cluster evaluation forms then recalculate the faculty performance based on clusters obtained.

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