Performance Evaluation of Some Clustering Algorithms and Validity Indices

By Ujjwal Maulik, Sanghamitra Bandyopadhyay. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

1,420 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f567df5c467b269a593874bcb5519ba4298b1e9e

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Citations in the corpus

An extensive comparative study of cluster validity indices (2013, methodology): “Sym-index (Symm) [30]: This index is an adaptation of the I index [19] based on the Point Symmetry-Distance.”
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I (2014, methodology): “In this regard, some cluster validity index [56] is used to evaluate the goodness of the clustering solution generated by the feature subset.”
Clustering of time series data - a survey (2005, background): “Maulik and Bandyopadhyay [28] evaluated four cluster validity indices.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “Other validity indices used in the bio-inspired clustering literature include CH Index [290,299],”
A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020, background): “where Ki denotes the number of clusters of Ci and it is adaptively decided by its covariance [23].”
Density-Based Clustering Validation (2014, methodology): “1 Relative Measures We compare our measure against five well-known relative measures from the literature, namely, Silhouette Width Criterion (SWC) [27], Variance Ratio Criterion (VRC) [4], Dunn [10], and Maulik-Bandyopadhyay (MB) [23].”
Mapping cropland-use intensity across Europe using MODIS NDVI time series (2016, methodology): “To identify the optimal number of clusters, we applied a sensitivity analyses with SOM clusters varying from 2×2 to 4×4 clusters (Maulik and Bandyopadhyay 2002) and used the Davies–Bouldin index that compares intra- and inter-cluster variability (Davies and Bouldin 1979) to pick the optimal cluster…”
Applications of artificial intelligence for disaster management (2020, neutral): “Clustering algorithms are used for pattern recognition by partitioning unlabeled data into multiple groups based on certain similarity features (Maulik and Bandyopadhyay 2002).”
Icasso: software for investigating the reliability of ICA estimates by clustering and visualization (2003, background): “4 can be additionally motivated by the fact that for two normalized zero mean vectors ui and uj their correlation coefficient rij = u T i uj and their Euclidean distance ‖ ui − uj ‖∝ √ 1 − rij [14].”
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015, background): “The peak of residential area is about 3 hours later than the second peak of transport, and the slope of these two peaks is almost identical.”
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization (2006, methodology): “Within the first approach, the indices by Calinski-Harabasz and Krzanowski-Lai have been repeatedly chosen as benchmarks [7], [25], [28], [33].”
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).”
Classification Approaches in Neighborhood Research: Introduction and Review (2011, methodology): “…term for such methods is cluster number estimation measures; they include among other approaches cross-validation techniques (cf. Dudoit and Fridlyand, 2002; Tibshirani and Walther, 2005; Fang et al., 2007) and cluster validity indices (Maulik and Bandyopadhyay, 2002; Kryszczuk and Hurley, 2010).”