By Petra Perner
This publication constitutes the refereed complaints of the sixteenth business convention on Advances in facts Mining, ICDM 2016, held in big apple, long island, united states, in July 2016.
The 33 revised complete papers offered have been conscientiously reviewed and chosen from a hundred submissions. the subjects variety from theoretical points of information mining to functions of information mining, equivalent to in multimedia facts, in advertising, in medication, and in technique keep watch over, undefined, and society.
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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Additional info for Advances in Data Mining. Applications and Theoretical Aspects: 16th Industrial Conference, ICDM 2016, New York, NY, USA, July 13-17, 2016. Proceedings
The spine is considered as a chain of MMSs. It was found that there is a linear relationship between herect and hleft/right of MMSs placed between the mid-points of each two successive vertebrae. We showed that by using the identiﬁed linear relationships, we reconstructed the spine curvatures that were good estimates of the actual curvatures. Besides, it was demonstrated that Cobb angle, the spinal mobility, and the spinal flexibility in the bending positions obtained from the estimated curvatures were also good estimates of the actual ones.
The mid-point of a vertebra is the intersection of the line drawn from the upper left corner to the lower right of the vertebral body and the line drawn from the upper right to the lower left of the vertebral body (the white circles in Fig. 2a) . Y-axis of G is parallel to the line joining the left and right pedicles of the lowest vertebra. Z-axis is parallel to the line that passes through the center of the upper and lower endplates of the lowest vertebra. X-axis deﬁnes the anterior direction.
The factors having strong relationship with the faculty performance should be focused to improve the quality of teaching. Some algorithms were applied to build the model to classify faculty performance based on evaluation factors. In , the empirical data were collected from the evaluation forms of an online system based on four groups of factors: subject knowledge, teaching skills and assessment methods, behavior towards students, communication skills. Models for classifying faculty performance using those factors obtained from M5P  and REP  algorithms were used to identify the determining factors of faculty performance.