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Algorithms for clustering data
Anil K. Jain,Richard C. Dubes +1 more
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The article was published on 1988-01-01 and is currently open access. It has received 8586 citations till now. The article focuses on the topics: Cluster analysis & Correlation clustering.read more
Citations
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Proceedings ArticleDOI
Active query selection for semi-supervised clustering
TL;DR: This work focuses on constraint (also known as query) selection for improving the performance of semi-supervised clustering algorithms, and presents an active query selection mechanism, where the queries are selected using a min-max criterion.
Journal ArticleDOI
Organizing large structural modelbases
Kuntal Sengupta,Kim L. Boyer +1 more
TL;DR: Presents a hierarchically structured approach to organizing large structural modelbases using an information theoretic criterion and introduces the node pointer lists, which are computed offline during modelbase organization.
Book ChapterDOI
On Fitting Mixture Models
TL;DR: This paper proposes a new minimum description length (MDL) type criterion, termed MMDL(f or mixture MDL), to select the number of components of the model, based on the identification of an "equivalent sample size", for each component, which does not coincide with the full sample size.
Book ChapterDOI
Unsupervised Learning and Clustering
TL;DR: This chapter begins with a review of the classic clustering techniques of k-means clustering and hierarchical clustering, and modern advances in clustering are covered with an analysis of kernel-based clusters and spectral clustering.
Journal ArticleDOI
Fast and exact out-of-core and distributed k -means clustering
TL;DR: This paper presents a new algorithm, called fast and exact k-means clustering (FEKM), which typically requires only one or a small number of passes on the entire dataset and provably produces the same cluster centres as reported by the original k-Means algorithm.
References
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Journal ArticleDOI
Shortest connection networks and some generalizations
TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.
Journal ArticleDOI
An examination of procedures for determining the number of clusters in a data set
TL;DR: A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters to provide a variety of clustering solutions.
Journal ArticleDOI
SLINK: An optimally efficient algorithm for the single-link cluster method
TL;DR: Sibson gives an O(n 2) algorithm for single-linkage clustering, and proves that this algorithm achieves the theoretically optimal lower time bound for obtaining a single- linkage dendrogram.