International Journal of Database Theory and Application

Running head: REPORT FOR DATA MINING TECHNIQUES FOR IS
REPORT
FOR
DATA MINING TECHNIQUES
WITH
IS
Name of the Student
Name of the University
Author Note:

DATA MINING TECHNIQUES FOR IS1
Introduction:
Considering the rapid growth of technology it is noticed that Data mining is one of the
trending technology that has majorly influenced the operational activities of several sensitive
industries. The data mining technique is such practice that uses a data analytics tool to analyze a
large set of data with the purpose to determine the relationship between the variables present in
the data set. [1]
Followed by this identification the primary objective of this paper is to analyze a research
paper that consists of a detailed study on the application of data mining on detecting breast
cancer, which will help to provide a detailed idea about the application of the nominated
technology. Along with this, it will also focus on the discussion of uses methods in the research
as well as research findings. Apart from these identifications, this paper will also consist of a
detailed elaboration on the issues that have been highlighted by the researcher. Lastly, will
conclude by stating the limitations of the study as well.
Background:
As compared to other diseases breast cancer is one of the harmful cancer types faced by
women and statistically it is proven that this is the second most harmful cancer that causes death
to women. This research has primarily focused on the urban cities of India with the purpose to
analyze the significance of breast cancer. Considering the global increment of the significance of
breast cancer it is noticed that women across the world are very much threatened by the
nominated disease. [2]
However, followed by these identifications it is recognized that it is very difficult to
analyze a large set of data related to this domain. Considering these aspects this paper has aimed
to discuss the aspect of breast cancer by analyzing a large set of banking data, retail data as well

DATA MINING TECHNIQUES FOR IS2
as telecommunication data by using the data mining techniques. Identifying the necessity of the
nominated research area the researcher has primarily focused on the data analysis by using
effective data mining techniques. [3]
After analyzing the above aspect it is noticed that in order to detect breast cancer it is first
essential to classify the patterns of breast cancer. Thus, in order to detect the pattern of breast
cancer in this research, the researcher has used three decision tree classifiers.
Methods:
In this selected research paper the researcher has focused on the investigation of breast
cancer by using three decision tree which includes the Sequential Minimal Optimization, IBK (K
nearest Neighbors classifier) and Best first trees. While analyzing the operations of Sequential
Minimal Optimization (SMO) is a support vector machine algorithm (SVM) which is one of the
fasted method training support vector machines. [4] Followed by these identifications it is
noticed that there are several benefits of SMO present in the nominated area which includes the
effective problem-solving category as well as analytical capability. Considering all of the above
aspects the researcher has declared SMO as one of the fastest liner SVM for light data set,
however, in this research we required to analyze large sets to data to get the desired result. [5]
Followed by the above identification it is noticed that K nearest Neighbor classification is
also one of the effective classifiers that classify data based on their similarities. While analyzing
these aspects it is noticed that these types of classifier that classifies the data by considering the
nearest neighbors of a selected point. Considering this identification it is noticed that this
technique is very much useful in determining unknown instances by identifying the nearest point
of the selected point. Along with these identifications, it is noticed that this technique is very
much effective in analyzing a large set of data. However, along with these identifications, it has