Applications of Data Mining (DM) in Science and Engineering: State of the art and perspectives
It provides a broad overview for researchers and practitioners, but is incremental as it synthesizes existing knowledge without new results.
This paper reviews the development of data mining techniques to handle increasing data volumes and distributed processing, discussing their applications across scientific and engineering fields.
The continuous increase in the availability of data of any kind, coupled with the development of networks of high-speed communications, the popularization of cloud computing and the growth of data centers and the emergence of high-performance computing does essential the task to develop techniques that allow more efficient data processing and analyzing of large volumes datasets and extraction of valuable information. In the following pages we will discuss about development of this field in recent decades, and its potential and applicability present in the various branches of scientific research. Also, we try to review briefly the different families of algorithms that are included in data mining research area, its scalability with increasing dimensionality of the input data and how they can be addressed and what behavior different methods in a scenario in which the information is distributed or decentralized processed so as to increment performance optimization in heterogeneous environments.