Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation

By S. Ray, R. Turi. 2000

756 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0ec132fce9971d1e0e670e650b58176dc7bf36da

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Citations in the corpus

kml and kml3d: R Packages to Cluster Longitudinal Data (2015, background): “Various indices have been suggested, either non-parametric (Cali´nski and Harabasz 1974; Ray and Turi 1999; Davies and Bouldin 1979; average silhouette, Rousseeuw 1987, cross validation, . . . ) or parametric (Schwarz 1978; Akaike 1974; Hurvich and Tsai 1989), but they are not completely…”
Differentially Private K-Means Clustering (2015, background): “Several heuristics and statistics [29, 32] have been proposed to determine the number of clusters k automatically.”
A new insight into land use classification based on aggregated mobile phone data (2013, methodology): “(4) Cluster the vectors of mobile phone data using FCM. (5) Post-process the clustering result by assigning each cluster to different land-use types.”
A new intrusion detection system using support vector machines and hierarchical clustering (2007, methodology): “However, cluster scattering measure [30] can be used to minimize the intra-cluster distance and maximize inter-cluster distance.”
Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. (2013, methodology): “All participants gave informed consent according to procedures approved by the University of Arizona’s Institutional Review Board.”
Quantitative evaluation of Tarocco sweet orange fruit shape using optoelectronic elliptic Fourier based analysis (2009, methodology): “Moreover, a procedure to estimate the optimal number of clusters with the k-means method was also presented by applying the validation procedures (Ray and Turi, 1999) on the EFA coefficients dataset.”
Understanding the new digital divide - A typology of Internet users in Europe (2011, methodology): “Second, to determine the number of clusters, we followed the procedures suggested by Ray and Turi (1999).”
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning (2018, unknown): “Previous works such as superpixel (Achanta et al. 2012) and K-Means clustering (Ray and Turi 1999) have explored the possibility of gathering pixels by measuring the similarity of low-level features.”
K-means clustering: a half-century synthesis. (2006, methodology): “Ray and Turi (2000) proposed a very similar method based on an index calculated by N 21 PK k¼1SSEk min½d 2ð x ðkÞ; x ðk ÞÞ for k – k ; ð21Þ where the numerator is the average within-cluster sum of squares and the denominator is the minimum distance between any two clusters.”