Soft set approach for categorical data clustering and maximal association rules mining

Mat Deris, Mustafa (2012) Soft set approach for categorical data clustering and maximal association rules mining. Other thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Recent advances in 'information technology have led to significant changes in today's world; both generating and collecting data have been increasing rapidly. This explosive growth h stored or transient data has generated an urgent need for new techniques that caa intelligently assist us in transforming the vast amounts of data into usell information and knowledge. Classification is one form of data analysis in data mining, which can be used to extract models describing important data classes. Researchers have proposed many classification methods. An important point is that each technique typically suits some pr~blemsb etter than others do, Thus, there is no universal data-mining method, In 1999, Mofodtsov initiated the concept of soft set theory as a mathematical tool for dealing with uncertainties. The sufi set theory has rr rich patentid for applications in several directions. However, application of soft set theory on data classification still not widely studies. There are few researches of data classification based on soft set theory. Although those methods are quite successful for data classification, however they are still need improvement. This research aim to propose a new approach to classified data based on soft set theory, to improve the accuracy and efficiency. It is called Fuzzy Soft Set Classifier (FSSC)

Item Type:Thesis (Other)
Subjects:Q Science > QA Mathematics > QA76 Computer software
ID Code:6891
Deposited By:Normajihan Abd. Rahman
Deposited On:10 May 2015 16:13
Last Modified:10 May 2015 16:13

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