MuDi-Stream: A multi density clustering algorithm for evolving data stream

• Main Authors: Amini, A., Saboohi, H., Herawan, T., Teh, Y.W.
• Format: Article
• Published: Elsevier 2016
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/18278/; https://doi.org/10.1016/j.jnca.2014.11.007

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.