Real Time Data Mining

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Data mining is about explaining the past and predicting the future by exploring and analyzing data. Data mining is a multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology. Although data mining algorithms are widely used in extremely diverse situations, in practice, one or more major limitations almost invariably appear and significantly constrain successful data mining applications. Frequently, these problems are associated with large increases in the rate of generation of data, the quantity of data and the number of attributes (variables) to be processed: Increasingly, the data situation is now beyond the capabilities of conventional data mining methods. The term Real Time is used to describe how well a data mining algorithm can accommodate an ever increasing data load instantaneously. Upgrading conventional data mining to real time data mining is through the use of a method termed the Real Time Learning Machine or RTLM. The use of the RTLM with conventional data mining methods enables Real Time Data Mining. The future of predictive modeling belongs to real time data mining and the main motivation in authoring this book is to help you to understand the method and to implement it for your applications.
To be truly visionary, leaders implementing analytical platforms should take advantage of real-time data analysis and management. Real-time analysis, combined with leading indicators and KPIs ...
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FILESIZE 5,19 MB
AUTHOR Saed Sayad
FILENAME Real Time Data Mining.pdf
PUBLISH DATE 05 Jan 2011
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Real-Time Data Mining Discussion

Data mining is about explaining the past and predicting the future by exploring and analyzing data. Data mining is a multi-disciplinary field which combines statistics, machine learning, artificial intelligence and database technology. Although data mining algorithms are widely used in extremely ...

Data Mining Applications & Trends - Tutorials Point

Thousands of data streams are generated in different industries like finance, health, internet, telecommunication, etc. The main problem is to analyze all these streams in real time to find correlation between streams, standard deviation, moving average, etc.

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