A Survey of Clustering Algorithms for Streaming

Data analysis of real time data streams continues to attract increased attention because of its importance in decision making, which can directly affect real life activities. It is no secret that these real time data streams are generated from numerous software applications and hardware creating a sizeable volume of data that is continuously generated, with evolving features over time. Data evolution with time is referred to as concept drift. Analysis of such streams is quite problematic due its volume. Clustering is not just a method of analyzing data streams of such size but it is additionally less complicated compared to other forms analysis. This paper takes a survey of some important clustering algorithms applicable to the analysis of data streams.