Towards Processing Multi-Dimensional Dynamic Data

Cluster and outlier detection has always been one of data mining research interests. Numerous approaches have been designed to find clusters and detect outliers in various types of data sets. In this paper, we present our research on analyzing data sets with constant changes. We design approaches to keep track of status of clusters, the movement of data points, and the updated group of outliers. Different from the traditional approaches which are focused on two-dimensional or low-dimensional data spaces, we aim to analyze data sets in multi-dimensional data spaces. We also propose to adjust the clusters and outliers simultaneously, since they are two concepts that are