Soft set approach for categorical data clustering and maximal association rules mining
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 i...
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| Format: | Thesis |
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2012
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| Online Access: | http://eprints.uthm.edu.my/6891/ http://eprints.uthm.edu.my/6891/1/FRGS_0736.pdf |
| Summary: | 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) |
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