Attribute value weighting in K-modes clustering for Y-short tandem repeats (Y-STR) surname

This paper evaluates Y-STR Surname data for attribute value weighting in k-Modes clustering algorithm. Three categories weighting schemas: (1) Relative Value Frequency (RVF); (2) Uncommon Attribute Value Matches (UAVM); (3) Hybrid weighting schema are evaluated for Y-STR Surname data. The overall re...

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Bibliographic Details
Main Authors: Seman, Ali, Bakar, Zainab Abu, Sapawi, Azizian Mohd
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithm design and analysis
attribute value weighting
categorical data
Classification algorithms
Clustering algorithms
Computer science
Helium
K-modes algorithm
Weight measurement
Y-STR
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Summary:This paper evaluates Y-STR Surname data for attribute value weighting in k-Modes clustering algorithm. Three categories weighting schemas: (1) Relative Value Frequency (RVF); (2) Uncommon Attribute Value Matches (UAVM); (3) Hybrid weighting schema are evaluated for Y-STR Surname data. The overall results show that the clustering accuracy of all methods produces in between 40-44% only. However, the idea of adapting a weighting schema still looks a promising method in order to improve the clustering accuracy for Y-STR data.
ISSN:2155-8973
DOI:10.1109/ITSIM.2010.5561471