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	Browse All Reviews > Computing Methodologies (I) > Pattern Recognition (I.5) > Clustering (I.5.3) > Algorithms (I.5.3...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 21 Reviews about "Algorithms (I.5.3...)": Date Reviewed    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... May 4 2023   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... May 8 2019   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... Aug 24 2016   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... Jan 20 2016   Data stream clustering: a survey Silva J., Faria E., Barros R., Hruschka E., Carvalho A., Gama J. ACM Computing Surveys 46(1): 1-31, 2013.  Type: Article Data streams, which are continuously generated at uncontrollable, rapid rates, are a relatively new form of temporal data object. Data stream clustering (DSC) is an unsupervised data mining process that looks for hidden patterns in dyn... Mar 4 2014   Interactive text document clustering using feature labeling Nourashrafeddin S., Milios E., Arnold D.  DocEng 2013 (Proceedings of the 2013 ACM Symposium on Document Engineering, Florence, Italy, Sep 10-13, 2013) 61-70, 2013.  Type: Proceedings Document clustering, the grouping of similar text documents, can be used in various applications. For example, it can facilitate the presentation of search results to web users. This paper introduces an interactive semi-supervised docu... Jan 23 2014   Unsupervised anomaly detection with minimal sensing Fine B.  ACM-SE 47 (Proceedings of the 47th Annual Southeast Regional Conference, Clemson, SC, Mar 19-21, 2009) 1-5, 2009.  Type: Proceedings Remote health monitoring is an emerging discipline with strong potential. It is also a critical application of modern-day networks of wireless sensor nodes--namely, wireless sensor networks (WSNs). For healthcare professionals... Oct 20 2009   On the use of human-computer interaction for projected nearest neighbor search Aggarwal C. (ed) Data Mining and Knowledge Discovery 13(1): 89-117, 2006.  Type: Article Aggarwal studies the problem of similarity-based nearest neighbor search in high-dimensional spaces and introduces an interactive user-adaptive nearest neighbor search system. It uses human and computer cooperation by exploiting their ... Apr 12 2007   The clustered causal state algorithm: efficient pattern discovery for lossy data-compression applications Schmiedekamp M., Subbu A., Phoha S. Computing in Science and Engineering 8(5): 59-67, 2006.  Type: Article Some heuristic methods for constructing finite Markov chains to approximate an observed data stream are described in this paper. The main idea is to apply these techniques to data compression where loss is allowed, such as with video s... Jan 24 2007   Data clustering with partial supervision Bouchachia A., Pedrycz W. Data Mining and Knowledge Discovery 12(1): 47-78, 2006.  Type: Article Partially or semisupervised clustering is a technique midway between unsupervised clustering, where no knowledge of the data structure is available, and supervised clustering, where the data pattern is supposed to be fully known. This ... Nov 8 2006       Display 510152550100 per page
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