MuDi-Stream: A multi density clustering algorithm for evolving data stream
Document Type
Article
Publication Date
1-1-2016
Abstract
Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams. However, existing density-based data stream clustering algorithms are not without problem. There is a dramatic decrease in the quality of clustering when there is a range in density of data. In this paper, a new method, called the MuDi-Stream, is developed. It is an online-offline algorithm with four main components. In the online phase, it keeps summary information about evolving multi-density data stream in the form of core mini-clusters. The offline phase generates the final clusters using an adapted density-based clustering algorithm. The grid-based method is used as an outlier buffer to handle both noises and multi-density data and yet is used to reduce the merging time of clustering. The algorithm is evaluated on various synthetic and real-world datasets using different quality metrics and further, scalability results are compared. The experimental results show that the proposed method in this study improves clustering quality in multi-density environments.
Keywords
Evolving data streams, Multi-density clusters, Core mini-clusters, Density grid
Divisions
fsktm
Funders
University of Malaya: UMRG vote no. RP002F-13ICT ,Ministry of Higher Education: High Impact Research (HIR) Grant, University of Malaya, no. UM.C/625/HIR/MOHE/SC/13/2
Publication Title
Journal of Network and Computer Applications
Volume
59
Publisher
Elsevier