Applications of Data Mining (DM) in Science and Engineering: State of the art and perspectives

TL;DR

This paper reviews the evolution, current state, and future perspectives of data mining applications in science and engineering, emphasizing techniques for efficient large-scale data analysis across various scientific domains.

Contribution

It provides a comprehensive overview of recent developments, algorithm families, scalability issues, and distributed processing methods in data mining for scientific research.

Findings

Data mining techniques have advanced significantly in recent decades.

Scalability and distributed processing are key challenges addressed.

The paper highlights potential future directions in the field.

Abstract

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.