Data Stream Mining Between Classical and Modern Applications: A Review

Abstract

Data mining (DM) is an amazing innovation with incredible potential to help organizations centre on the main data in the information they have gathered about the conduct of their clients and expected clients. It finds data inside the information that questions and reports can't viably uncover. For the most part, DM is the way toward examining information from alternate points of view and summing up it into helpful data - data that can be utilized to expand income, reduces expenses, or both. There are four types of DM: 1) Classification and regression, 2) Clustering, 3) Association Rule Mining, and 4) Outlier/Anomaly Detection. Tending to the velocity part of Big Data (BD) has as of late pulled in a lot of revenue in the investigation local area because of its critical effect on information from pretty much every area of life like medical services, financial exchange, and interpersonal organizations, and so on. Many research works have investigated this velocity issue through mining data streams. Most existing data stream mining research centres on adjusting the primary classifications of approaches, methods and methods for static information to the dynamic information circumstance. This research explores widely the current writing in the field of data stream mining and recognizes the fundamental preparing units supporting different existing methods. This study not simply benefits examiner to make strong assessment subjects and separate gaps in the field yet moreover helps specialists for DM and BD application structure headway.

Keywords

• data stream mining,,
• ,،,؛mining algorithms,,
• ,،,؛big data