Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset

Abstract

A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods.

Association Rule Analysis of Influencing Factors of Literature Curriculum Interest Based on Data Mining

“…The innovations in the research process of this article are (1) it uses data mining technology to analyze association rules and establish an association rule model in studying the influencing factors of interest in literature courses. By analyzing the factors affecting literature courses according to this process, we can get significantly related courses, further analyze and explain the relevant courses and obtain statistical significance factors, to analyze the correlation between courses and waiting courses [ 3 ]. (2) Relevant data have been refined through well-known fusion schemes, set minimum support and confidence thresholds to 0.2 and 0.83, respectively, and generate more than 500 rules.…”