Overview Of Streaming-Data Algorithms

Due to recent advances in data collection techniques, massive amounts of data are being collected at anextremely fast pace. Also, these data are potentially unbounded. Boundless streams of data collected fromsensors, equipments, and other data sources are referred to as data streams. Various data mining taskscan be performed on data streams in search of interesting patterns. This paper studies a particular datamining task, clustering, which can be used as the first step in many knowledge discovery processes. Bygrouping data streams into homogeneous clusters, data miners can learn about data characteristicswhich can then be developed into classification models for new data or predictive models for unknownevents. Recent research addresses the problem of data-stream mining to deal with applications thatrequire processing huge amounts of data such as sensor data analysis and financial applications. Forsuch analysis, single-pass algorithms that consume a small amount of memory are critical.