The use of sampling to cluster large data sets

Hopke, Philip K.; Kaufman, Leonard

doi:10.1016/0169-7439(90)80135-s

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…Indeed, for S values that correspond to computation time approximately equal to the run time of GMCA, the latter still performs better on all data sets. On the other hand, large n values, which lead to a quadratic increase in the computation time, generally improve the results, which agrees with findings of (Hopke and Kaufman, 1990). For n values that correspond to computation time of the GMCA, the CLARA can perform as good as the GMCA on the Simulate15 and Yeast384 data sets, however, it still performs worse than the GMCA for the data sets of Simulate30 and Yeast2945, which have comparatively large search space.…”

Section: Further Comparisonssupporting

confidence: 89%

A genetic k-medoids clustering algorithm

Sheng

Liu

2006

J Heuristics

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We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the genetic algorithm to fine-tune the search. Further, variable length individuals that encode different number of medoids (clusters) are used for evolution with a modified Davies-Bouldin index as a measure of the fitness of the corresponding partitionings. As a result the proposed algorithm can efficiently evolve appropriate partitionings while making no a priori assumption about the number of clusters present in the datasets. In the experiments, we show the effectiveness of the proposed algorithm and compare it with other related clustering methods.

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Section: Further Comparisonssupporting

confidence: 89%

A genetic k-medoids clustering algorithm

Sheng

Liu

2006

J Heuristics

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“…While also here a range of quality measures exist, i.e., the Dunn index [15] or the Calinski-Harabasz score [10], we chose silhouette coefficient [24], because it is represents an established and intuitive measure for both cohesion and separation of clusters. In our evaluation section (Section V-A) we present a comparative evaluation of the quality computations based on the Dunn index and the Calinski-Harabasz score.…”

Section: Prototypical Implementationmentioning

confidence: 99%

Visual Pattern-Driven Exploration of Big Data

Behrisch

Krueger

Lekschas

et al. 2018

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)

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Pattern extraction algorithms are enabling insights into the ever-growing amount of today’s datasets by translating reoccurring data properties into compact representations. Yet, a practical problem arises: With increasing data volumes and complexity also the number of patterns increases, leaving the analyst with a vast result space. Current algorithmic and especially visualization approaches often fail to answer central overview questions essential for a comprehensive understanding of pattern distributions and support, their quality, and relevance to the analysis task. To address these challenges, we contribute a visual analytics pipeline targeted on the pattern-driven exploration of result spaces in a semi-automatic fashion. Specifically, we combine image feature analysis and unsupervised learning to partition the pattern space into interpretable, coherent chunks, which should be given priority in a subsequent in-depth analysis. In our analysis scenarios, no ground-truth is given. Thus, we employ and evaluate novel quality metrics derived from the distance distributions of our image feature vectors and the derived cluster model to guide the feature selection process. We visualize our results interactively, allowing the user to drill down from overview to detail into the pattern space and demonstrate our techniques in two case studies on Earth observation and biomedical genomic data.

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“…The sample size preferred for Hierarchical Cluster Analysis is not more than 200 samples [3]. Reference [4] mentioned large data sets can be problems with Agglomerative Hierarchical Cluster Analysis. An alternative to Agglomerative Hierarchical Cluster Analysis for more than 200 data is given by various forms of nonhierarchical Cluster Analysis [4].…”

Section: Research Site and Instrumentmentioning

confidence: 99%

A Comparison Between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis for Identifying Tourists Segments

Rashid

2012

IIUMEJ

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Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis test. The result of the comparison used for segmenting tourists of Kapas Island. The statistical software SPSS has been applied to analyze data of this research. The result from Kruskal-Wallis test shows Complete Linkage is more useful in identifying tourists segments. Keywords : Agglomerative Hierarchical Cluster Analysis, Single Linkage, Complete Linkage, Kruskal-Wallis test, tourists

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The use of sampling to cluster large data sets

Cited by 12 publications

References 9 publications

A genetic k-medoids clustering algorithm

A genetic k-medoids clustering algorithm

Visual Pattern-Driven Exploration of Big Data

A Comparison Between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis for Identifying Tourists Segments

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