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absence of medical diagnosis evidences, it is difficult for the experts to
opine about the grade of disease with affirmation. Generally many tests are
done that involve clustering or classification of large scale data. However
many tests could complicate the main diagnosis process and lead to the
difficulty in obtaining the end results, particularly in the case where many
tests are performed. This kind of difficulty could be resolved with the aid of
machine learning techniques. In this research, we present a comparative study
of different classification techniques using three data mining tools named
WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance
of different classification techniques for a set of large data. A fundamental
review on the selected techniques is presented for introduction purpose. The
diabetes data with a total instance of 768 and 9 attributes (8 for input and 1
for output) will be used to test and justify the differences between the
classification methods. Subsequently, the classification technique that has the
potential to significantly improve the common or conventional methods will be
suggested for use in large scale data, bioinformatics or other general
Many companies like credit card, insurance,
bank, retail industry require direct marketing. Data mining can help those institutes
to set marketing goal. Data mining techniques have good prospects in their target
audiences and improve the likelihood of response. In this work we have investigated
two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms.
The goal of this work is to predict whether a client will subscribe a term deposit.
We also made comparative study of performance of those two algorithms. Publicly
available UCI data is used to train and test the performance of the algorithms.
Besides, we extract actionable knowledge from decision tree that focuses to take
interesting and important decision in business area.
The study was conducted to explore aggression in boys and girls as related to their academic achievement and residential background in Bangladesh. Stratified random sampling technique was used and total 80 respondents constituted the sample of the study. They were equally divided into boys and girls. Each group was again equally divided into high and low grade. Each subgroup was again equally divided into urban and rural residential background. Thus the study used a 2 × 2 × 2 factorial design consisting of two levels of gender (boy/girl), two levels of academic achievement (high grade/low grade) and two levels of residential background (urban/rural). The Bengali version of measure of aggressive behavior (Rahman, A. K. M. R., 2003) originally developed by Buss and Perry (1992) was used for the collection of data. It was found that regardless of gender, boys expressed more aggression than girls. Similarly, regardless of academic achievement, students with high academic grade will show more aggressive behavior than low academic grade students. Finally, students of urban areas will not show significantly more aggressive behavior than rural areas students. Thus the differential treatment in gender, academic achievement and residential background provides a new dimension in understanding aggression in rural and urban boys and girls.