An analytical framework for data stream mining techniques based on challenges and requirements [article]

Mahnoosh Kholghi (Department of Electronic, Computer and IT, Islamic Azad University, Qazvin Branch, Qazvin, Iran and member of Young Researchers Club) and Mohammadreza Keyvanpour (Department of Computer Engineering Alzahra University Tehran, Iran)
<span title="2011-05-10">2011</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly
more &raquo; ... lly challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.
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