Data clustering - theory, algorithms, and applications

By Guojun Gan, Chaoqun Ma, Jianhong Wu. 2007

657 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e9bfd209ee6d22bcc3fdebf7616f9939f123882c

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Citations in the corpus

Communities in Networks (2009, methodology): “There are also a few classical divisive techniques, in which one starts with the full graph and breaks it up to find communities [39,46,110].”
Algorithms for hierarchical clustering: an overview, II (2017, methodology): “For further information see Jain and Dubes (1988) where chapter 4 is dedicated to cluster validity, Gan et Jain and Dubes, 1988, and Gan et al. 2007) shows some of the equations for cluster validation indices.”
Time-series clustering - A decade review (2015, background): “Complete reviews and comparisons of some popular techniques exist in the literature [217–220].”
A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data (2015, methodology): “Sensitive to outliers [27,31].”
Highly Comparative Feature-Based Time-Series Classification (2014, background): “Instead, this problem has conventionally been addressed by deﬁning a distance metric between time series that involves comparing the sequential values directly.”
Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, methodology): “After careful consideration, k -means, a robust and widely used clustering technique, was adopted for clustering load proﬁles. a) k-means clustering algorithm [26]: Let D be a data set with m instances, and let C 1 , C 2 , . . . , C k be the k disjoint clusters of D .”
A roadmap for the computation of persistent homology (2015, background): “, [33, 35, 40, 55] and many other references), recent mathematical developments are shedding new light on such ‘traditional’ ideas and forging new approaches of their own.”