Table 2.

Brief introduction of data mining methods.

Methods	Advantages	Disadvantages	Literatures
Logistic regression	Multifunction	Needing of sample size	Luo et al. [46]
Bayesian networks	Utilization of incomplete and inaccurate data	Needing of preceding researches as guidance	Qu et al. [33]
Rough sets theory	Without priori information; simplicity; handling ambiguous and uncertain information	Needing of self-development	Zhang et al. [28]
Association rules mining	Supporting indirect data mining	Nonselectivity; subjectivity	Wu et al. [26]
Set pair analysis	Suitability for changing systems	Handicap in handle relatively precise problems	Li et al. [45]
Structural equation modeling	Analyzing the causality between the latent variables	Needs of 200 samples at least	Chen et al. [44]
Cluster analysis	Minimization errors caused by subjective judgment	Too much calculation; handicap in clustering data with multidimensions and multilevel	Gu et al. [30]
Decision trees	Handling in nonnumeric data; Simplicity	Maybe misleading	Zhong et al. [35]
Principal component analysis	Dimension reduction; holism	Less specificity	Lu et al. [39]
Partial least squares method	Specificity	Handicap in deciding principal component	Van Wietmarschen et al. [17]
Artificial neural network	Simplicity; nonlinear	Handicap in obtaining the hidden information	Sun et al. [37]
Entropy cluster algorithm	Little demand on variances' types; analysis on any statistical dependence of the variances	Needing of self-development	Wang et al. [47]
Factor analysis	Correction capability; views to latent variables	Absence of domination and relationship between primary and secondary	Wang et al. [42]
Support vector machine	Classification without representing the feature space explicitly	Expressing the more complex prior information; analyzing limited samples	Yang et al. [48]