NDLI: A novel measure for validating clustering results applied to road traffic

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A novel measure for validating clustering results applied to road traffic

Content Provider	ACM Digital Library
Author	Sinaoui, Kaouther Blibech Naïja, Yosr
Abstract	The clustering validation and clustering interpretation are the two last steps of clustering process. The validation step permits to evaluate the goodness of clustering results using some measures. Valid results are then generally interpreted and used in cluster analysis. The validity measures are classified into three categories: unsupervised measures, supervised measures and relative measures. Several supervised measures have been proposed to perform supervised evaluation such as entropy, purity, F-measure, Jaccard coefficient and Rand statistic. Generally, these measures evaluate results according to class labels. However, they are not always able to distinguish interpretable clusters because most of them depends on the number of labels. This paper proposes a new supervised evaluation measure - called "homogeneity degree"- that permits to merge the steps of validation and interpretation. Our measure is applied to a real traffic data set and is used to interpret some traffic situations. Comparison with other evaluation measures shows the performance of our proposal.
Starting Page	105
Ending Page	113
Page Count	9
File Format	PDF
ISBN	9781605586687
DOI	10.1145/1601966.1601984
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Validity Supervised measure External criteria Clustering
Content Type	Text
Resource Type	Article

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