Paper Title:
Identifying the Mastery Concepts in Linear Algebra by Using FCM-CM Algorithm
  Abstract

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. Gustafson-Kessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were developed to detect non-spherical structural clusters by employing Mahalanobis distance in objective function, however, both of them need to add some constrains for Mahalanobis distance. In this paper, the authors’ improved Fuzzy C-Means algorithm based on common Mahalanobis distance (FCM-CM) is used to identify the mastery concepts in linear algebra, for comparing the performances with other four partition algorithms; FCM-M, GG, GK, and FCM. The result shows that FCM-CM has better performance than others.

  Info
Periodical
Edited by
Ran Chen
Pages
3897-3901
DOI
10.4028/www.scientific.net/AMM.44-47.3897
Citation
H. C. Liu, Y. K. Yu, J. M. Yih, C. C. Chen, "Identifying the Mastery Concepts in Linear Algebra by Using FCM-CM Algorithm", Applied Mechanics and Materials, Vols. 44-47, pp. 3897-3901, 2011
Online since
December 2010
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