Shou-Jen Chang-Chien

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Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure that had(More)
This article presents a new similarity measure for LR-type fuzzy numbers. The proposed similarity measure is based on a defined metric between LR-type fuzzy numbers. It is known that an exponential operation is highly useful in dealing with the classical Shannon entropy and cluster analysis. We adopted, therefore, the exponential operation on this metric.(More)
A castor oil model induced diarrhea was used to evaluate dose regimens of the standard antidiarrheal polycarbophil. The study population consisted of 100 healthy volunteers, divided into five groups of 20 each, in whom diarrhea was induced by 120 ml flavored 36.4% castor oil. The polycarbophil dose regimens evaluated were 1, 1.5, 2, or 3 gm at 30-min(More)
Clustering methods have been widely applied in various areas. The objective of clustering is to find the data structure and also to partition the data set into groups with similar individuals. A mean shift-based method had been proposed as a clustering algorithm. However, most mean shift-based clustering algorithms are used for numeric data. In this paper,(More)
The mean shift clustering algorithm is a useful tool for clustering numeric data. Recently, Chang-Chien et al. [1] proposed a mean shift clustering algorithm for circular data that are directional data on a plane. In this paper, we extend the mean shift clustering for directional data on a hypersphere. The three types of mean shift procedures are(More)
Directional data on a hypersphere has been used in biology, geology, medicine, meteorology and oceanography. Clustering is a useful tool for the analysis of these data on the unit hypersphere. In general, the EM algorithm with a mixture of von Mises distributions is the most commonly used clustering method for 2-dimensional directional data on the plane.(More)
The query processing optimization is done using an efficient clustering method for the purpose of fast retrieval of the queries. The main desire of a user is to query regarding the point of interest such as nearby restaurants, cafes, etc. , The Location Based Service (LBS) enables the user to access their information about their POI (Point Of Interest).(More)
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