The ability of data mining to provide predictive information
derived from huge databases became an effective tool in the hands
of companies and individuals، allowing them to focus on areas that
are important to them from the massive data generated
by the
march of their daily lives. Along with the increasing importance of
this science there was a rapidly increasing in the tools that produced
to implement the theory concepts as fast as possible. So it will be
hard to take a decision on which of these tools is the best to
perform the desired task. This study provides a comparison
between the two most commonly used data mining tools according
to opinion polls، namely: Rapidminer and R programming language
in order to help researchers and developers to choose the best suited
tool for them between the two. Adopted the comparison on seven
criteria: platform، algorithms، input/output formats، visualization،
user’s evaluation، infrastructure and potential development، and
performance by applying a set of classification algorithms on a
number of data sets and using two techniques to split data set: cross
validation and hold-out to make sure of the results. The Results
show that R supports the largest number of algorithms، input/output
formats، and visualization. While Rapidminer superiority in terms
of ease of use and support for a greater number of platforms. In
terms of performance the accuracy of classification models that
were built using the R packages were higher. That was not true in
some cases imposed by the nature of the data because we did not
added any pre-processing stage. Finally the preference option in
any tool is depending on the extent of the user experience and
purpose that the tool is used for
This study aimed to indicate the level of interest in the application of concepts and
data mining tools in the management of banking operations areas and the interest
components of the environment and the application of the concepts of data mining
tools in the management of banking operations in commercial banks of Jordan. To
achieve these goals, the researcher used the descriptive analytical approach based on
the questionnaire distributed to members of the community study. The researcher
found that the percentage of interest among members of the community study on the
application of the concepts of data mining operations the management of banking,
was high in general, where the arithmetic mean is generally equal to (4.005). And
that the order of fields that may be seen when you search in the application of the
concepts of data mining and addressed by this study, have been of importance and
level of interest by the members of the population of the study, as follows: the
working environment of knowledge with information technology "has obtained the
highest average, was the average the arithmetic of this axis is equal to (4.02),
followed by the center of "opportunities to enhance knowledge systems with the
development environment systems research and retrieval of data.
In this paper we introduce a comparison for some of data mining algorithm for traffic accidents analysis.
We start by describing available data for entry by analyzing the structure of statistical reports in Lattakia traffic directorate, and proceed
to data mining stage which enables us to smart study of factors that play roles in traffic accident and find its inter-relations and importance for causing traffic accident.
That comes after building data warehouse upon the database we built to store the data we gathered.
In this research we list a some of models was tested which is a sample of a many cases we checked to have the research results.
Association Rules is an important field in Data Mining, which is
used to discover useful knowledge from a massive databases.
Association Rules have been used to extract the information from
the database transactions, and Apriori Algorithm is a pra
ctical
application for Association Rules and it is used to find frequent
itemsets from database transactions. In this paper, we present a
new improving on Apriori Algorithm by reduction generating of
candidate itemsets and this leads to improving efficiency Apriori
Algorithm.
In this research, we offered a new and simple way of
Handwriting Characters Recognition. This way extracts positions of
the black points from binary images (black, white) according to
certain coordinates which are used in the stages of training an
d
testing. The extracted positions are stored in a database according
to appropriate structure for predictive data mining.
We used training data to build a predictive model which helps
in Recognition testing data depending on the data stored in the
database. We have conducted a number of tests on different
samples of handwriting character images. We got accurate results,
within the required conditions.
The main goal of data mining process is to extract information and
discover knowledge from huge databases, where the clustering is
one of the most important functionalities which can be done in this
area. There are many of clustering algorithms an
d methods, but
determining or estimating the number of clusters which should be
extracted from a dataset is one of the most important issues most of
these methods encounter it. This research focuses on the problem of
estimating number of clusters in the case of agglomerative
hierarchical clustering. We present an evaluation of three of the
most common methods used in estimating number of clusters.
Through this study we will explain the application of data mining
and business intelligence using the data existed in the library of
the Arab International University. This data has been linked to the
data of the students on the academic system of
the university.
The study will also answer questions that affect the work of the
educational institution in general and the library in particular,
propose solutions to improve the work of the library and its
services, enhance library working methods, and specify indicators
related to the role of information resources in the educational
operation.
This paper introduces a new algorithm to solve some problems
that data clustering algorithms such as K-Means suffer from.
This new algorithm by itself is able to cluster data without the
need of other clustering algorithms.
The objective of this paper is presenting an educational data
mining case study, by applying data mining techniques.
Data Mining
التنقيب في البيانات
التنقيب في بيانات المؤسسات التعليمية
برنامج ذكاء الأعمال الخاص بشركة مايكروسوفت
خوارزمية مايكروسوفت لأشجار القرار
خوارزمية مايكروسوفت للتجميع و الكشف عن الحالات الشاذة
خوارزمية مايكروسوفت لقواعد الارتباط
Educational Data Mining
SQL Server Business Intelligence Development Studio
Microsoft Decision Trees
Microsoft Clustering
Outlier Detection
Microsoft Association Rules
المزيد..
The Research Aims:
Syrian organizations keep large amounts of information and data about their
personnel in their IT systems. This information, however, is often left unutilized or
may be analyzed through statistical methods. In this study, DM is
considered a
solution for analyzing HR data and explore knowledge from data stored in some
Syrian organization through two major stages:
Stage A: Using results of Semi-Annual performance evaluation process to build
prototype showed in (Fig. 6) to accomplish two tasks:
1. Building a models to predict appropriate job function for an employee
through majority principle and using high accuracy result to increase the
number of training data and make it self-learning model.
2. Choose most important attributes that used in classify methods to use it in
personnel selection and recruitment.
Stage B: Using data of Time & Attendance to analysis personnel activity through
clustering methods and building many meaningful groups.
