Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)

By Guojun Gan, Chaoqun Ma, Jianhong Wu. 2007

1,230 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5dd31b5b0031c2f3b7c19e82cba24b0cca2a5932

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In Artificial Intelligence

Citations in the corpus

Communities in Networks (2009, methodology): “To determine community placement, we used the Fruchterman-Reingold graph visualization [45], a force-directed layout method that is related to maximizing a quality function known as modularity [92].”
An exact algorithm for the multicriteria ordered clustering problem (2012, background): “The problem of grouping ‘similar’ objects into homogeneous groups has been extensively studied in the literature [1] and is commonly encountered in different fields [2,3] such as finance, health care, agriculture, marketing, and image processing.”
Coupled nominal similarity in unsupervised learning (2011, methodology): “One of the clustering approaches is the k-modes (KM ) algorithm [7], designed to cluster categorical data sets.”
Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review (2017, neutral): “s the following drawback, "if two data vectors have no attribute values in common, they may have a smaller distance than the other pair of data vectors containing the same attribute values" [22], so that, this distance was adopted. AD(x;y) = v u u t1 n Xn i=1 (x i y i)2 5.10Mean Censored E”
Algorithms for hierarchical clustering: an overview (2012, methodology): “54 • GRIDCLUS: Proposed by Schikute, 55 GRIDCLUS is a hierarchical algorithm for clustering very large datasets.”
K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data (2015, methodology): “When applying traditional clustering algorithms to the domain of bioinformatics, additional challenges are faced due to prevalent existence of nonlinear correlations in the high dimensional space [9].”
Data stream clustering: A survey (2013, background): “see [Kogan 2007; Gan et al. 2007; Xu and Wunsch 2009] — and out of the scope of this paper.”
Geographic Constraints on Social Network Groups (2010, methodology): “We used k-means clustering [18] which aims to partition the set of data points into k clusters such that each point belongs to the cluster with the nearest mean.”
Community detection in graphs (2009, methodology): “, singular value decomposition and principal component analysis (Gan et al., 2007).”
Relative clustering validity criteria: A comparative overview (2010, background): “Data clustering is a fundamental conceptual problem in data mining, in which one aims at determining a finite set of categories to describe a data set according to similarities among its objects [1] [2] [3].”
Adaptive Graph Encoder for Attributed Graph Embedding (2020, methodology): “3 Evaluation Metrics & Parameter Settings Tomeasuretheperformanceofnodeclusteringmethods,weemploy three metrics: Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) [9]. For link prediction, we partition the datasets following GAE, and report Area Under Curve (AUC)”