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Data clustering is an important task for data management because it groups similar data into clusters and acquires significant knowledge. K-means is one of the popular clustering algorithms; however, there are several weaknesses such as cluster quality often depended on initial centers and too sensitive to an outlier. To address the problems, this study proposed a new method of initial centers selection based on data density and a novel approach of outlier detection based on data distance. I conducted some experiments to evaluate the methods. For the new method of initial centers selection, I compared the number of iterations and the Silhouette scores from this method and the traditional K-means. For the outlier detection system, I measured the system performance by using a confusion matrix. As the results, the system of the study outperformed the traditional K-means because of higher speed and great accuracy acquired.

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This study proposes a methodology that integrates the epsilon constraint method (EC) and artificial neural network (ANN) to determine shelter location-allocation. Since shelter location-allocation is a critical part of disaster response stage, fast decision-making is very important. A multi-objective optimization model is formulated to simultaneously minimize total cost and minimize total evacuation time. The proposed model is solved by EC because it generates the optimal solutions without intervention of decision-makers during the solution process. However, EC requires intensive computational time, especially when dealing with large-scale data. Thus, ANN is combined with EC to facilitate prompt decision-making and address the complexity. Herein, ANN is supervised by the optimal solutions generated by EC. The applicability of the proposed methodology is demonstrated through a case study of shelter allocation in response to flooding in Surat Thani, Thailand. It is plausible to use this proposed methodology to improve disaster response for the benefit of victims and decision-makers.