Data Mining Techniques in Database Systems

Year 2017, Volume: 2 Issue: 1, 43 - 50, 25.02.2017

Ledion Lico

Abstract

At the current stage the
technologies for generating and collecting data have been advancing rapidly.
The main problem is the extraction of valuable and accurate information from
large data sets. One of the main techniques for solving this problem is Data Mining.
Data mining (DM) is the process of identification and extraction of useful
information in typically large databases. DM aims to automatically discover the
knowledge that is not easily perceivable. It uses statistical analysis and  artificial intelligence (AI) techniques  together to address the issues. There are
different types of tasks associated to data mining process. Each task can be
thought of as a particular kind of problem to be solved by a data mining algorithm.
The main types of tasks performed by DM algorithms are:

•   Classification:

•   Association:

•   Clustering:

•   Regression:

•   Anomaly
Detection:

•   Feature
Extraction

• Time Series Analyses

In this paper we will perform a survey of
the techniques above. A secondary goal of our paper is to give an overview of
how DM is integrated in Business Intelligence (BI) systems  .BI refers to a set of tools used for
multidimensional data analysis, with the main purpose to facilitate decision
making. One of the main components of BI systems is OLAP. The main OLAP
component is the data cube which is a multidimensional database model that with
various techniques has accomplished an incredible speed-up of analyzing and
processing large data sets. We will discuss the advantages of integrating DM
tools in BI systems.

Keywords

Data Mining, BI, OLAP, AI

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