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Advances in data stream mining
Author(s) -
Gaber Mohamed Medhat
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.52
Subject(s) - data stream mining , computer science , data science , streaming data , data stream , data mining , key (lock) , phone , streams , computer security , telecommunications , computer network , linguistics , philosophy
Mining data streams has been a focal point of research interest over the past decade. Hardware and software advances have contributed to the significance of this area of research by introducing faster than ever data generation. This rapidly generated data has been termed as data streams . Credit card transactions, Google searches, phone calls in a city, and many others\are typical data streams. In many important applications, it is inevitable to analyze this streaming data in real time. Traditional data mining techniques have fallen short in addressing the needs of data stream mining. Randomization, approximation, and adaptation have been used extensively in developing new techniques or adopting exiting ones to enable them to operate in a streaming environment. This paper reviews key milestones and state of the art in the data stream mining area. Future insights are also be presented. © 2011 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining