Paper Title
Online Clustering of Evolving Data Stream based on Adaptive Chebychev Distance

Abstract
Density-based methods have appeared as a valuable category for the clustering of evolving data streams. Although a number of density-based algorithms have recently been developed for the clustering of data streams, these algorithms are not without their issues. The quality of the clustering is dramatically reduced when the distance function is used. This paper presents an online clustering algorithm based on density called the Clustering of Evolving data streams based on the adaptive Chebychev distance (CEC). This algorithm keeps a summary of evolving data streams in the form of Core Micro-Clusters (CMCs), and also introduces a Chebychev distance to calculate the distance between the incoming data point and the centre of the CMCs in an online manner. The experimental results were compared with another online density-based clustering method called the CEDAS algorithm. The recommended method was then implemented to both real and artificial data streams using different quality metrics. The proposed method provides an effective solution for improving the quality of clusters. Keywords - Chebyshev, Density-based clustering, Core Micro-Clusters, Evolving data stream.