Cluster analysis and outlier detection are two continuously rising topics in data mining area, which fact connect to each other deeply. structure is vulnerable outliers; inversely, outliers the points belonging none of any clusters. Unfortunately, most existing studies do not notice coupled relationship between these tasks handle them separately. In this article, we consider joint cluster probl...

Cluster analysis and outlier detection are strongly coupled tasks in data mining area. Cluster structure can be easily destroyed by few outliers; on the contrary, the outliers are defined by the concept of cluster, which are recognized as the points belonging to none of the clusters. However, most existing studies handle them separately. In light of this, we consider the joint cluster analysis ...

BACKGROUND A general trend towards positive patient-reported evaluations of hospitals could be taken as a sign that most patients form a homogeneous, reasonably pleased group, and consequently that there is little need for quality improvement. The objective of this study was to explore this assumption by identifying and statistically validating clusters of patients based on their evaluation of ...

Many studies of outlier detection have been developed based on the cluster-based outlier detection approach, since it does not need any prior knowledge of the dataset. However, the previous studies only regard the outlier factor computation with respect to a single point or a small cluster, which reflects its deviates from a common cluster. Furthermore, all objects within outlier cluster are as...