Evolution to Big Data Analytics Techniques
Due to the increment of data volume have made the
well-known data mining algorithms unsuitable for such data siz-
es. Therefore, many studies have currently been directed towards
improvements that data mining techniques can handle Big Data.
Big data analytic techniques are concerned with several data
mining functions, where the most important functions are: asso-
ciation rules mining and classification tree analysis.
In [12] paper, it analyzed the main data mining tasks
which can adopt big data analytics techniques and “V” dimen-
sions of big data.
Table 1 represents a summary of the analysis done for
the evolution of data mining tasks to big data analytics.
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Conclusion
Now, we are in big data time, and there is a growing
demand for tools which can process and analyze it. Big data
analytics deals with extracting valuable information from that
massive data which can’t be handled by traditional data mining
tools. In this paper, we discuss big data mining, it characteris-
tics, challenges and algorithms used to deal with big data mining
efficiently
. Also provide some techniques of big data mining:
Hadoop framework and MapReduce framework. Big data min-
ing can be in many different applications in enterprises, social
networks and mobile clouds. Finally, we discuss some of big data
analytics techniques and its evolution.
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The copyright format belongs to IEEE 2012.
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