Incremental Gaussian Mixture Clustering for Data Streams

TL;DR

This paper introduces an incremental clustering algorithm for data streams that uses entropy minimization to form clusters and detect anomalies, demonstrated on 2-D datasets.

Contribution

It presents a novel incremental Gaussian mixture clustering method that effectively identifies clusters and anomalies in streaming data.

Findings

Successful clustering of 2-D streaming datasets

Effective anomaly detection far from known clusters

Demonstrated applicability to large-scale data streams

Abstract

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.