International Journal of Computer Applications Technology and Research
Volume 5– Issue 3, 172 - 175, 2016, ISSN:- 2319–8656
www.ijcat.com 172
A Comparative Study of Various Data Mining
Techniques: Statistics, Decision Trees and Neural
Networks
Balar Khalid
Department of Computer Science
Hassan II University-FMPC
Casablanca, Morocco
Naji Abdelwahab
Department of Computer Science
Hassan II University-ENSET
Mohammedia, Morocco
Abstract: In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
Keywords: Data mining, Statistics, Logistic Regression, Decision Trees and Neural Networks.
1. INTRODUCTION
Extraction useful information from data is very far easier from
collecting them. Therefore many sophisticated techniques,
such as those developed in the multi- disciplinary field data
mining are applied to the analysis of the datasets. One of the
most difficult tasks in data mining is determining which of the
multitude of available data mining technique is best suited to a
given problem. Clearly, a more generalized approach to
information extraction would improve the accuracy and cost
effectiveness of using data mining techniques.
Therefore, this paper proposes a new direction based on
evaluation techniques for solving data mining tasks, by using
three techniques: Statistics, Decision Tree and Neural
Networks.
The aim of this new approach is to study those techniques and
their processes and to evaluate data mining techniques on the
basis of: the suitability to a given problem, the advantages and
disadvantages, and the consequences of choosing any
technique, [5].
2. DATA MINING TOOLS
Data mining, the extraction of hidden predictive information
from large databases, is a powerful new technology with great
potential to help companies focus on the most important
information in their data warehouses [6]. Data mining tools
predict future trends and behaviors allowing businesses to
make proactive knowledge driven decisions. Data mining
tools can answer business question that traditionally were too
time consuming to resolve.
They scour database for hidden patterns, finding predictive
information that experts may miss because it lies outside their
expectations.
3. SELECTED DATA MINING
TECHNIQUES
A large number of modeling techniques are labeled "data
mining" techniques [7]. This section provides a short review
of a selected number of these techniques. Our choice was
guided the focus on the most currently used models. The
review in this section only highlights some of the features of
different techniques and how they influence, and benefit from.
We do not present a complete exposition of the mathematical
details of the algorithms, or their implementations.
Although various different techniques are used for different
purposes those that are of interest in the present context [4].
Data mining techniques which are selected are Statistics,
Decision Tree and Neural Networks.
3.1 Statistical Techniques
By strict definition "statistics" or statistical techniques are not
data mining. They were being used long before the term data
mining was coined. However, statistical techniques are driven
by the data and are used to discover patterns and build
predictive models.
Today people have to deal with up to terabytes of data and
have to make sense of it and glean the important patterns from
it. Statistics can help greatly in this process by helping to
answer several important questions about their data: what
patterns are there in database?, what is the chance that an
event will occur?, which patterns are significant?, and what is
a high level summary of the data that gives some idea of what
is contained in database?
In statistics, prediction is usually synonymous with regression
of some form. There are a variety of different types of
regression in statistics but the basic idea is that a model is
created that maps values from predictors in such a way that
the lowest error occurs in making a prediction.
The simplest form of regression is Simple Linear Regression
that just contains one predictor and a prediction. The
relationship between the two can be mapped on a two
dimensional space and the records plotted for the prediction
values along the Y axis and the predictor values along the X
axis. The simple linear regression model then could be viewed
as the line that minimized the error rate between the actual
prediction value and the point on the line [2].
Adding more predictors to the linear equation can produce
more complicated lines that take more information into
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