Impact of Dimensionality on the Evaluation of Stream Data Clustering Algorithms

Abstract

Handling stream data is a tedious task. Recently numerous techniques are presented for analysing stream data. Stream data clustering is one of the important tasks in stream data mining. A number of application programming interfaces (APIs) are available for implementing the stream data clustering. These APIs can handle the stream data of any dimension. The objective of the presented paper is to explore the impact of dimensionality over the existing standard data stream clustering algorithms. Selected standard data stream clustering algorithms are compared for different dimensions of stream using six performance parameters, namely adjusted Rand index, Dunn index, entropy, F1 measure, purity and within cluster sum of square measure.