An Attempt to Dynamically Process Multi-Dimensional 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, the authors present their research on analyzing data sets with constant changes. They 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, they aim to analyze data sets in multi-dimensional data spaces. They also propose to adjust the clusters and outliers simultaneously, since they are two concepts that are closely related.