A COMPREHENSIVE REVIEW ON DATA STREAM MINING TECHNIQUES FOR DATA CLASSIFICATION; AND FUTURE TRENDS
Keywords:Data Stream Mining, Rapid Development, Classification, Clustering, D-Stream, HP Stream, ANNCAD, CDM, AWSOM, CLustream, Approximate Frequent Counts
Data Mining is a developing interdisciplinary control managing Data Reclamation and Data Stream Mining techniques, whose subject is gathering, overseeing, processing, breaking down, and visualizing the huge volume of organized or unstructured data. Data stream mining indicates how to look at Unknown patterns from a massive amount of data over algorithms. It has experienced quick improvement with significant progress in math, statistics, data science, and computer science domains. Data streams are commonly generated by various sources such as sensor networks, social media feeds, financial transactions, online retail, network traffic, and many other applications. The gathered data could be additionally utilized for various purposes, for example, execution assessment, irregularity discovery, change identification, or issue finding of the operating systems. This data stream analysis is done using different data stream mining techniques. This paper provides a broad overview of the distinct approaches used for data stream mining. Initially, we studied the different techniques of data stream mining. Next, we discuss the different clustering and classification techniques and their benefits. Then we examine the evaluation of different data stream mining techniques results that some techniques are feasible for real-time data streams and some of not. This study provides a complete understanding of techniques and their benefits. The studies done so far need to be sufficiently exhaustive for data mining techniques, so future work is needed to assess which technique is feasible for real-time data streams.
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