Doctoral Thesis / Dissertation from the year 2020 in the subject
Computer Science - Commercial Information Technology, Symbiosis
International University, language: English, abstract: Data mining is
coined one of the steps while discovering insights from large amounts of
data which may be stored in databases, data warehouses, or in other
information repositories. Data mining is now playing a significant role
in seeking a decision support to draw higher profits by the modern
business world. Various researchers studied the benefits of data mining
processes and its adoption by business organizations, but very few of
them have discussed the success factors of decision support projects.
The Research Hypothesis states the involvement of the decision tree
while adopting accuracy of classification and while emphasizing the
impact factor or importance of the attributes rather than the
information gain. The concept of involvement of impact factor rather
than just accuracy can be utilized in developing the new algorithm whose
performance improves over the existing algorithms. We proposed a new
algorithm which improves accuracy and contributing effectively in
decision tree learning. We presented an algorithm that resolves the
above stated problem of confliction of class. We have introduced the
impact factor and classified impact factor to resolve the conflict
situation. We have used data mining technique in facilitating the
decision support with improved performance over its existing companion.
We have also addressed the unique problem which have not been addressed
before. Definitely, the fusion of data mining and decision support can
contribute to problem-solving by enabling the vast hidden knowledge from
data and knowledge received from experts. We have discussed a lot of
work done in the field of decision support and hierarchical
multi-attribute decision models. Ample amount of algorithms are
available which are used to classify the data in datasets. Most
algorithms use
