Cluster Data Streams with Noisy Variables

Abstract

Clustering algorithms are important methods widely used in mining data streams because of their abilities to deal with infinite data flows. Although these algorithms perform well to mining latent relationship in data streams, most of them suffer from loss of cluster purity and become unstable when the inputting data streams have too many noisy variables. In this article, we propose a clustering algorithm to cluster data streams with noisy variables. The result from simulation shows that our proposal method is better than previous studies by adding a process of variable selection as a component in clustering algorithms. The results of two experiments indicate that clustering data streams with the process of variable selection are more stable and have better purity than those without such process. Another experiment testing KDD-CUP99 dataset also shows that our algorithm can generate more stable result.

Keywords:

• Clustering algorithms
• Data streams
• Noisy variables
• Variable selection

Mathematical Subject Classification:

• TP181

Funding

This article is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China. The project is “Extension Study on Joint Evaluation for Repeated Measurements and Survival Time Data Based on High Dimensional Joint Model” (No. 13XNH190).