Nonparametric genetic clustering: comparison of validity indices

By Sanghamitra Bandyopadhyay, Ujjwal Maulik. IEEE Transactions on Systems, Man & Cybernetics. Systems, 2001

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/483b1ff07b430ee974815a6dde252d67d6747557

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Citations in the corpus

Validating the independent components of neuroimaging time series via clustering and visualization (2004, background): “There are numerous reviews and studies on the many agglomeration strategies and cluster validity indices (see, e.g., Bandyopadhay and Maulik, 2001; Bezdek and Pal, 1998; Everitt, 1993; Gordon, 1987; Maulik and Bandyopadhay, 2002).”
A new cluster validity measure and its application to image compression (2004, unknown): “Many different cluster validity measures have been proposed [5,7,28,29,30,31,32,33,34,35,36, 37 ,38,39], such as the Dunn’s separation measure [5,28], Bezdek’s partition coefficient [29], Xie–Beni’s separation measure [30], Davies–Bouldin’s measure [31], Gath–Geva’s measure [7], etc.”
Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, background): “We apply projection methods related to multidimensional scaling (MDS) as suggested in [5] to approximate the original dissimilarities between esti- mates by Euclidean distances in two dimensions.”
Computational cluster validation in post-genomic data analysis (2005, background): “objectives can be tackled by general-purpose meta-heuristics such as simulated annealing, tabu search and evolutionary algorithms (Bandyopadhyay and Manlik, 2001; Rayward-Smith et al., 1996).”
Survey of clustering algorithms (2005, background): “We offer more detailed discussion on how to identify appropriate number of clusters, which is particularly important in cluster validity, in the last part of the section.”
On fuzzy cluster validity indices (2007, result): “And we must state that the Dataset_4_3, Dataset_5_2, Dataset_6_2 and Dataset_10_2 data sets are similar to data sets used in [3,37,38].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, result): “In [112,113] Bandopadhay and Maulik have developed”
A Survey of Evolutionary Algorithms for Clustering (2009, background): “: Proportional selection [6] has been used by several authors (e.g., Krovi [84]; Lucasius et al. [96]; Murthy and Chowdhury [107]; Estivill-Castro and Murray [39]; Fränti et al. [48]; Maulik and Bandyopadhyay [100]; Kivijärvi et al. [79]; Bandyopadhyay and Maulik [10]; Krishna and Murty [83]; Lu et ”