Data Stream Mining
DOI:
https://doi.org/10.37591/rtpl.v5i1.1565Abstract
A data stream can be considered as an ordered sequence of data items, where the elements of the series continuously arrive as time progresses. Data stream mining is the procedure of extracting knowledge structures from such continuous, rapid data records. Mining data streams nurtures new problems for the data mining community regarding how to mine continuous high-speed data items that you can only have one look at. Due to this reason, traditional data mining approach demands to be changed and to discover patterns or knowledge from data streams , it is necessary to develop on-line , single-scan, multi-dimensional , multilevel stream processing and analysis methods. Most conventional data mining techniques have to be adapted to fit with the nature of the data streams, because of the underlying resource restraints in terms of running time and memory. The intention of the paper is to present an introduction to data stream mining as it is a new and growing field of research. First we will provide the concept of data streams and then we will focus on data stream mining as a very rich subject of research.
Keywords: Data stream, single-scan, multi-scan, clustering and classification
Cite this Article
Aabiroo Bader. Data Stream Mining. Recent Trends in Programming Languages. 2018; 5(1): 1–5p.
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