Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Comparison of clustering methods for clinical databases
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Duplicate Record Detection: A Survey
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
To detect database records containing approximate and exact duplicates because of data entry error or differences in the detailed schemas of records from multiple databases or for some other reasons is an important line of research. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of Silhouette width, Calinski & Harbasz index (pseudo F-statistics) and Baker & Hubert index (驴 index) algorithms for exact and approximate duplicates. In this paper, a comparative study and effectiveness of these three cluster validation techniques which involve measuring the stability of a partition in a data set in the presence of noise, in particular, approximate and exact duplicates are presented. Silhouette width, Calinski & Harbasz index and Baker & Hubert index are calculated before and after inserting the exact and approximate duplicates (deliberately) in the data set. Comprehensive experiments on glass, wine, iris and ruspini database confirms that the Baker & Hubert index is not stable in the presence of approximate duplicates. Moreover, Silhouette width, Calinski and Harbasz index and Baker & Hubert indice do not exceed the original data indice in the presence of approximate duplicates.