On Clustering Validation Techniques

By Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis. 2001

2,538 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Is data clustering in adversarial settings secure? (2013, background): “The initial set of clusters C, as in [23], is selected as the partitioning that minimizes the value of the Davies-Bouldin Index (DBI) [15], a measure that characterizes dispersion and closeness of clusters.”
Unsupervised Deep Embedding for Clustering Analysis (2015, background): “…machine learning in terms of feature selection (Boutsidis et al., 2009; Liu & Yu, 2005; Alelyani et al., 2013), distance functions (Xing et al., 2002; Xiang et al., 2008), grouping methods (Mac-Queen et al., 1967; Von Luxburg, 2007; Li et al., 2004), and cluster validation (Halkidi et al., 2001).”
ST-DBSCAN: An algorithm for clustering spatial-temporal data (2007, unknown): “Most studies in KDD [12] focus on discovering clusters from ordinary data (non-spatial and non-temporal data), so they are impractical to use for clustering spatial–temporal data.”
Computational cluster validation in post-genomic data analysis (2005, methodology): “The correlation between the two matrices is commonly referred to as cophenetic correlation, matrix correlation, or standardized Mantel Statistic [31].”
Understanding of Internal Clustering Validation Measures (2010, unknown): “It measures the degree of difference between clusters [6] [7].”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “With clustering, we can identify and summarize interesting patterns and correlations in the underlying data [27].”
Unsupervised K-Means Clustering Algorithm (2020, methodology): “…including k-means, are employed to give different numbers of clusters with associated cluster memberships, and then these clustering results are evaluated by multiple validity measures to determine the most practically plausible clustering results with the estimated number of clusters [13].”
Criticality of metals and metalloids (2015, background): “RMSSTD measures the homogeneity of the new cluster formed at each stage of the hierarchical joining process, SPR measures the loss of homogeneity that results from the merging of two clusters, and RS measures the degree of homogeneity between clusters (195).”
Multiscale Embedded Gene Co-expression Network Analysis (2015, background): “age silhouette width, Normalized Gamma statistics, Dunn’s index and separation index [68, 69] to evaluate quality of clustering results across k 2 [2,|A| − 1].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, unknown): “index [295,296], root-mean-square standard deviation index [295,296], RS index [297], PBM index [300] and SV index [301].”
Dynamic clustering using particle swarm optimization with application in image segmentation (2006, methodology): “Dunn’s index suffers from two problems in that it is computationally expensive and sensitive to the presence of noise [13].”
Clustering algorithms: A comparative approach (2016, unknown): “Many measurements have been defined for quantifying such similarity [30], we compare the Jaccard index [31], Adjusted Rand index [32], FowlkesMallows index [33] and Normalized mutual information [34].”
Cluster validity measurement techniques (2006, unknown): “Several validity indices were developed and introduced in various works [5][6][7][8][9].”
An extensive comparative study of cluster validity indices (2013, methodology): “The process of estimating how well a partition fits the structure underlying the data is known as cluster validation [1].”
Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces (2010, methodology): “To solve this problem, instead of prefiltering HTTP traffic against legitimate websites, we apply a signature pruning process by testing the signature set S′i against a large dataset of legitimate traffic and discard the signatures that generate false positives.”
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks (2018, background): “Clustering has been extensively studied in unsupervised learning with multifarious approaches seeking efficient algorithms [24], problem specific distance metrics [29], validation [12] and the like.”
A personalized search engine based on Web‐snippet hierarchical clustering (2005, background): “posals for evaluating flat clustering [13, 23], it is still open the definition of a mathematical evaluation which takes into”
A comparison of extrinsic clustering evaluation metrics based on formal constraints (2008, unknown): “Another approach to define evaluation metrics for clustering is considering statistics over pairs of items (Halkidi et al. 2001; Meila 2003).”