An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Sentiment analysis

A comparison of machine learning techniques for customer churn prediction

Much research in the last two decades has demonstrated that human responses deviate from the performance deemed normative according to various models of decision making and rational judgment (e.g., the basic axioms of utility theory). This gap between the normative and the descriptive can be interpreted as indicating systematic irrationalities in human cognition. However, four alternative interpretations preserve the assumption that human behavior and cognition is largely rational. These posit that the gap is due to (1) performance errors, (2) computational limitations, (3) the wrong norm being applied by the experimenter, and (4) a different construal of the task by the subject. In the debates about the viability of these alternative explanations, attention has been focused too narrowly on the model response. In a series of experiments involving most of the classic tasks in the heuristics and biases literature, we have examined the implications of individual differences in performance for each of the four explanations of the normative/descriptive gap. Performance errors are a minor factor in the gap; computational limitations underlie non-normative responding on several tasks, particularly those that involve some type of cognitive decontextualization. Unexpected patterns of covariance can suggest when the wrong norm is being applied to a task or when an alternative construal of the task should be considered appropriate.

Individual differences in reasoning: Implications for the rationality debate?-Open Peer Commentary-Differences, games, and pluralism

Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data.

Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations

Customer retention in telecommunication companies is one of the most important issues in customer relationship management, and customer churn prediction is a major instrument in customer retention. Churn prediction aims at identifying potential churning customers. Traditional approaches for determining potential churning customers are based only on customer personal information without considering the relationship among customers. However, the subscribers of telecommunication companies are connected with other customers, and network properties among people may affect the churn. For this reason, we proposed a new procedure of the churn prediction by examining the communication patterns among subscribers and considering a propagation process in a network based on call detail records which transfers churning information from churners to non-churners. A fast and effective propagation process is possible through community detection and through setting the initial energy of churners (the amount of information transferred) differently in churn date or centrality. The proposed procedure was evaluated based on the performance of the prediction model trained with a social network feature and traditional personal features.

Kyoungok Kim

Papers

Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations

Improved churn prediction in telecommunication industry by analyzing a large network

Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

A hybrid classification algorithm by subspace partitioning through semi-supervised decision tree

Exploring the difference between ridership patterns of subway and taxi: Case study in Seoul