Incremental Gaussian Mixture Clustering for Data Streams
This addresses the need for analyzing large-volume data streams in various application domains, but it appears incremental as it builds on existing clustering methods.
The paper tackled the problem of clustering and anomaly detection in streaming datasets by presenting an algorithm that uses entropy minimization to define and update clusters, demonstrating its effectiveness on 2-D datasets.
The problem of analyzing data streams of very large volumes is important and is very desirable for many application domains. In this paper we present and demonstrate effective working of an algorithm to find clusters and anomalous data points in a streaming datasets. Entropy minimization is used as a criterion for defining and updating clusters formed from a streaming dataset. As the clusters are formed we also identify anomalous datapoints that show up far away from all known clusters. With a number of 2-D datasets we demonstrate the effectiveness of discovering the clusters and also identifying anomalous data points.