In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this r…

Since outlier detection is applicable to various fields such as the financial, telecommunications, medical, and commercial industries, its importance is radically increasing. Receiving such great attention has led to the development of many detection methods, …

In this research, we employed various data mining techniques to build predictive models for win-loss prediction in Korean professional baseball games. The historical data containing information about players and teams was obtained from the official materials t…

An important and challenging problem in data clustering is the determination of the best number of clusters. A variety of estimation methods has been proposed over the years to address this problem. Most of these methods depend on several nontrivial assumption…

Recommender systems rely on the opinions of many users to predict the preferences of potential customers. These systems have been broadly used to make quality recommendations to increase sales. However, recommender systems are vulnerable to even small data inp…

In binary classifications, a decision tree learned from unbalanced data typically creates an important challenge related to the high misclassification rate of the minority class. Assigning different misclassification costs can address this problem, though usua…

Identifying clusters of arbitrary shapes remains a challenge in the field of data clustering. We propose a new measure of cluster quality based on minimizing the penalty of disconnection between objects that would be ideally clustered together. This disconnect…

This paper introduces a new methodology for discovering patterns in foodborne disease outbreaks using a data-driven approach. Specifically, our approach uses three data mining methods, namely attribute selection, decision tree learning, and association rule di…

The two of the most famous techniques in collaborative filtering (CF) are the so-called User-Based CF and Item-Based CF. In this paper, we claim that each of them takes only one-directional information from the user-item ratings matrix to generate recommendati…

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorit…

Collaborative filtering based on voting scores has been known to be the most successful recommendation technique and has been used in a number of different applications. However, since voting scores are not easily available, similar techniques should be needed…

A loss function approach to a multiresponse problem is considered, when process parameters are regarded as random variables. The variation of each response may be amplified through so called propagation of error (POE), which is defined as the standard deviatio…

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation which may amplify the variance of response. It is called POE(propagation of error), which is defined as the standard deviation of the transmitted …