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Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 87 - Number 15 |
| Year of Publication: 2014 |
| Authors: V. Sidda Reddy, M. Narendra, K. Helini |
10.5120/15284-3915
|
V. Sidda Reddy, M. Narendra, K. Helini . Knowledge Discovery from Static Datasets to Evolving Data Streams and Challenges. International Journal of Computer Applications. 87, 15 ( February 2014), 22-25. DOI=10.5120/15284-3915
Abstract
Mining data streams has recently become an important active research work and more widespread in several fields of computer science and engineering. It has proven successfully in many domains such as wireless sensor networks, ATM transactions, search engines, web analysis and weather monitoring. Data steams can be considered a subfield of machine learning, data mining and knowledge discovery. Data Mining is a step in the process of knowledge discovery from large amount of data. Traditional data mining techniques can not be easily applied to the data stream mining due to unique characteristics of data streams. In this research work, we will survey the main techniques and applications of data mining and data stream mining. We then study, the computational and miming challenges in particular, on-line mining of continuous, high-speed massive data streams.
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