Cluster validation analysis on attribute relative of soft-set theory
Data clustering on categorical data pose a difficult challenge since there are no-inherent distance measures between data values. One of the approaches that can be used is by introducing a series of clustering attributes in the categorical data. By this approach, Maximum Total Attribute Relative (MT...
| Main Authors: | , , , |
|---|---|
| Format: | Book Section |
| Published: |
Springer International Publishing
2017
|
| Subjects: | |
| Online Access: | DOI:10.1007/978-3-319-51281-5_1 DOI:10.1007/978-3-319-51281-5_1 |
| Summary: | Data clustering on categorical data pose a difficult challenge
since there are no-inherent distance measures between data values. One
of the approaches that can be used is by introducing a series of clustering
attributes in the categorical data. By this approach, Maximum Total
Attribute Relative (MTAR) technique that is based on the attribute relative
of soft-set theory has been proposed and proved has better execution
time as compared to other equivalent techniques that used the same
approach. In this paper, the cluster validity analysis on the technique
is explained and discussed. In this analysis, the validity of the clusters
produced by MTAR technique is evaluated by the entropy measure using
two standards dataset: Soybean (Small) and Zoo from University California
at Irvine (UCI) repository. Results show that the clusters produce by
MTAR technique have better entropy and improved the clusters validity
up to 33%. |
|---|