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Algorithms for clustering data

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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.

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Citations
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Book ChapterDOI

A Generalization-Based Approach to Clustering of Web Usage Sessions

TL;DR: In this article, the clustering of Web usage sessions based on the access patterns is studied, where access patterns of Web users are extracted from Web server log files, and then organized into sessions which represent episodes of interaction between the Web users and the Web server.
Journal ArticleDOI

Fuzzy computing for data mining

TL;DR: This study introduces unsupervised learning (clustering) where optimization is supported by the linguistic granules of context, thereby giving rise to so-called context-sensitive fuzzy clustering.
Journal ArticleDOI

On voting-based consensus of cluster ensembles

TL;DR: This paper shows that a recently introduced cumulative voting scheme is a special case corresponding to a linear regression method, and uses a randomized ensemble generation technique for extracting the consensus clustering from the aggregated ensemble representation and for estimating the number of clusters.
Patent

Meta-descriptor for multimedia information

TL;DR: In this article, meta-descriptors are generated for multimedia information in a repository by extracting the descriptors from the multimedia information and clustering the metadata information based on the descriptor.
ReportDOI

Concept Indexing: A Fast Dimensionality Reduction Algorithm With Applications to Document Retrieval and Categorization

TL;DR: Experimental results show that the dimensionality reduction computed by CI achieves comparable retrieval performance to that obtained using LSI, while requiring an order of magnitude less time.
References
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Book

Clustering Algorithms

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.