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Applying spectral biclustering to mortality data [PDF]
Risks, 2017 We apply spectral biclustering to mortality datasets in order to capture three relevant aspects: the period, the age and the cohort effects, as their knowledge is a key factor in understanding actuarial liabilities of private life insurance companies ...
Gabriella Piscopo, Marina Resta
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Biometrics, 2016 SummaryIn the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high-dimensional genomic data motivates this work.
Chi, Eric C. +2 more
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An approximation ratio for biclustering [PDF]
Information Processing Letters, 2008 The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on the rows and on the columns of the ...
Kai Puolamäki +2 more
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GENE BICLUSTERING ON LARGE DATASETS USING FUZZY C-MEANS CLUSTERING
ICTACT Journal on Soft Computing, 2022 The current study employs biclustering to alleviate some of the drawbacks associated with gene expression data grouping. Different biclustering algorithms are used in this study to detect unique gene activity in various contexts and reduce the ...
M Ramkumar +4 more
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Biclustering with heterogeneous variance [PDF]
Proceedings of the National Academy of Sciences, 2013 In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis.
Chen, Guanhua +2 more
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Journal of King Saud University: Computer and Information Sciences, 2022 Background: Ensemble biclustering comprises a class of biclustering algorithms that generates a consensus, better-quality partition/s as output. This concept has emerged from the fusion of existing biclustering methods hybridized upon selected aspects ...
Bhawani Sankar Biswal +2 more
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Recommendation System with Biclustering
Big Data Mining and Analytics, 2022 The massive growth of online commercial data has raised the request for an automatic recommender system to benefit both users and merchants. One of the most frequently used recommendation methods is collaborative filtering, but its accuracy is limited by the sparsity of the rating dataset.
Jianjun Sun, Yu Zhang
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Austrian Journal of Statistics, 2020 Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data.
Alhadi Bustamam +3 more
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, 2022 Feature dependence-based biclustering with application to real-life ...
namitaML
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Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis [PDF]
, 2006 The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques.
Mitra, S. +14 more
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