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Browse All Reviews > Computing Methodologies (I) > Pattern Recognition (I.5) > Clustering (I.5.3)
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1-10 of 73
Reviews about "Clustering (I.5.3)":
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Date Reviewed |
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 A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data
Murray J., Philipson P. Computational Statistics & Data Analysis 170(1): 1-15, 2022. Type: Article
Many longitudinal clinical studies involve the repeated periodic measurement of continuous responses over a period to detect any changes that might occur in the condition of the individual, such as events of interest like survival after 90 days. I...
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May 4 2023 |
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A new block matching algorithm based on stochastic fractal search
Betka A., Terki N., Toumi A., Hamiane M., Ourchani A. Applied Intelligence 49(3): 1146-1160, 2019. Type: Article
Block matching is an important technique for applications involving motion estimation, such as in video surveillance, TV broadcasting, video games, and so on. To improve the efficiency and effectiveness of block matching algorithms, th...
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May 8 2019 |
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Improved analysis of complete-linkage clustering
Gro&bgr;wendt A., Röglin H. Algorithmica 78(4): 1131-1150, 2017. Type: Article
The authors consider the problem of clustering n points in ℝd into k clusters, where the metric on ℝd has yet to be s...
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Jul 31 2018 |
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Scalable density-based clustering with quality guarantees using random projections
Schneider J., Vlachos M. Data Mining and Knowledge Discovery 31(4): 972-1005, 2017. Type: Article
Efficient clustering techniques are required for knowledge discovery in large databases. The efforts of scientists have contributed to the development of many clustering algorithms....
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Oct 30 2017 |
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A novel probabilistic clustering model for heterogeneous networks
Deng Z., Xu X. Machine Learning 104(1): 1-24, 2016. Type: Article
Following up works tagged as link mining, in which we fully consider the links between objects in the data mining process [1], this paper addresses the clustering task when performed on heterogeneous networks....
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Aug 31 2016 |
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Sublinear algorithms for big data applications
Wang D., Han Z., Springer International Publishing, New York, NY, 2015. 85 pp. Type: Book (978-3-319204-47-5)
“Big data” is the current buzzword pervading both the worlds of academia and industry. Between hype and distractions, research and innovation on big data proceeds at a very quick pace; in particular, the main subjec...
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Aug 24 2016 |
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Semi-supervised hybrid clustering by integrating Gaussian mixture model and distance metric learning
Zhang Y., Wen J., Wang X., Jiang Z. Journal of Intelligent Information Systems 45(1): 113-130, 2015. Type: Article
Zhang et al. propose a semi-supervised clustering algorithm, called SSCGD, addressing a specific class of such techniques: probabilistic clustering. The algorithm optimizes a given Gaussian mixture model (GMM) by adding, on the one han...
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Jan 20 2016 |
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An improved data characterization method and its application in classification algorithm recommendation
Wang G., Song Q., Zhu X. Applied Intelligence 43(4): 892-912, 2015. Type: Article
Classification is an active research problem, and numerous classification algorithms have been proposed over the past few years. Some algorithms perform better than others, based on the dataset. The “no silver bullet̶...
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Jan 20 2016 |
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A modified kernel clustering method with multiple factors
Zhu C., Gao D. Pattern Analysis & Applications 18(4): 871-886, 2015. Type: Article
An optimization technique, a modified kernel clustering method, is presented in this paper, making a contribution to the improvement of the performance of kernel clustering....
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Jan 6 2016 |
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Subjectively interesting alternative clusterings
Kontonasios K., De Bie T. Machine Learning 98(1-2): 31-56, 2015. Type: Article
In clustering problems, there are many unknowns and many potential desirable sets of clusters. Any complex set of data contains patterns related to many potential questions, and there are likely many potential ways to cluster data for ...
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Jun 11 2015 |
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