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CLUSTERING METHODS FOR DATA STREAM MINING 


Abstract

Abstract: The process of finding patterns fromhuge data sets using various algorithms of machinelearning, statistics, and database systems is known as Data mining. Data Stream mining is one of thepromising areas of research in Data Mining. A data stream being an ordered sequenceof instances, Stream Mining is the process of deriving knowledgestructures from uninterrupted and speedy data records from these instances. Incredible amount of datais generated with the increasing use of Internet in this digital era, which hasto be analysed. There is a need to process the data as soon as it becomesavailable as it is continuous and bulky in nature which cannot be stored for along time. Various algorithms are available for data stream mining, which performssingle or less number of scans. With the recent advancement in Internet ofThings (IOT), huge data streams are generated, thus making stream mining one ofthe most promising area of research. This paper is a review of different Clusteringmethods used for data stream mining.

Keywords: DataMining, Data streams, Clustering

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