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Biclustering Learning of Trading Rules
IEEE Transactions on Cybernetics, 2015Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators.
Huang, Qinghua +3 more
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Biclustering of High-throughput Gene Expression Data with Bicluster Miner
2012 IEEE 12th International Conference on Data Mining Workshops, 2012During recent years, many biclustering algorithms have been developed for the analysis of gene expression data to complement and expand the capabilities of traditional clustering methods. With biclustering, genes with similar expression profiles can be identified not only over the whole data set but also across subsets of experimental conditions ...
Andras Kiraly +3 more
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Boolean Representation for Exact Biclustering
Fundamenta Informaticae, 2018Biclustering is a branch of data analysis, whereby the goal is to find two–dimensional subgroups in a matrix of scalars. We introduce a new approach for biclustering discrete and binary matrices on the basis of boolean function analysis. We draw the correspondence between non–extendable (maximal with respect to inclusion) exact biclusters and prime ...
Michalak, Marcin, Ślȩzak, Dominik
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Possibilistic Biclustering for Discovering Value-Coherent Overlapping $$\delta $$-Biclusters
2014The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Microarrays have been used to study different kinds of biological processes.
Pradipta Maji, Sushmita Paul
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A Novel Biclustering Algorithm for Discovering Value-Coherent Overlapping ¿-Biclusters
2008 16th International Conference on Advanced Computing and Communications, 2008The biclustering method is a very useful tool for analyzing gene expression data when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. It focuses on finding a subset of genes and a subset of experimental conditions that together exhibit coherent behavior.
Chandra Das +2 more
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Biclustering Sparse Binary Genomic Data
Journal of Computational Biology, 2008Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with ...
Van Uitert, M. (author) +2 more
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BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION
Biocomputing 2016, 2015The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues.
Sahand, Khakabimamaghani, Martin, Ester
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Biclustering via Sparse Singular Value Decomposition
Biometrics, 2010SummarySparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row–column associations within high‐dimensional data matrices. SSVD seeks a low‐rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both
Lee, Mihee +3 more
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2014
This thesis presents a new ant-optimized biclustering technique known as SNAP biclustering, which runs faster and produces results of superior quality to previous techniques. Biclustering techniques have been designed to compensate for the weaknesses of classical clustering algorithms by allowing cluster overlap, and allowing vectors to be grouped for ...
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This thesis presents a new ant-optimized biclustering technique known as SNAP biclustering, which runs faster and produces results of superior quality to previous techniques. Biclustering techniques have been designed to compensate for the weaknesses of classical clustering algorithms by allowing cluster overlap, and allowing vectors to be grouped for ...
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Biclustering Multivariate Time Series
2017Sensor networks are able to generate large amounts of unsupervised multivariate time series data. Understanding this data is a non-trivial task: not only patterns in the time series for individual variables can be of interest, it can also be important to understand the relations between patterns in different variables. In this paper, we present a novel
Ricardo Cachucho +2 more
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