ABSTRACT:
Data mining consists of evolving set of
techniques that can be used to extract valuable information and knowledge from
massive volumes of data. Data mining research &tools have focused on
commercial sector applications. Only a fewer data mining research have focused
on scientific data. This paper aims at further data mining study on scientific
data. This paper highlights the data mining techniques applied to mine for
surface changes over time (e.g. Earthquake rupture). The data mining techniques
help researchers to predict the changes in the intensity of volcano’s. This
paper uses predictive statistical models that can be applied to areas such as
seismic activity, the spreading of fire. The basic problem in this class of
systems is unobservable dynamics with respect to earthquakes. The space-time
patterns associated with time, location and magnitude of the sudden events from
the force threshold are observable. This paper highlights the observable space
time earthquake patterns from unobservable dynamics using data mining
techniques, pattern recognition and ensemble forecasting. Thus this paper gives insight on how data
mining can be applied in finding the consequences of earthquakes and hence
alerting the public
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