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Boolean Representation for Exact Biclustering

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Biclustering 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 implicants of a discernibility function describing the data. We present also the results of boolean-style clustering of the artificial discrete image data. Some possibilities of utilizing basic image processing techniques for this kind of input to the biclustering problem are discussed as well.
Wydawca
Rocznik
Strony
275--297
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
autor
  • Institute of Informatics, University of Warsaw, ul. Banacha, 02-097 Warsaw, Poland
Bibliografia
  • [1] Nguyen HS. Approximate Boolean Reasoning: Foundations and Applications in Data Mining. Transactions on Rough Sets V. 2006;p. 334-506. doi:10.1007/11847465 16.
  • [2] Pawlak Z, Skowron A. Rough Sets and Boolean Reasoning. Information Sciences. 2007;177(1):41-73. URL https://doi.org/10.1016/j.ins.2006.06.007.
  • [3] Hartigan JA. Direct Clustering of a Data Matrix. Journal of the American Statistical Association. 1972; 67(337):123-129.
  • [4] Tanay A, Sharan R, Shamir R. Biclustering algorithms: A survey. Handbook of computational molecular biology. 2005;9(1-20):122-124.
  • [5] Chagoyen M, Carmona-Saez P, Shatkay H, Carazo JM, Pascual-Montano A. Discovering semantic features in the literature: a foundation for building functional associations. BMC Bioinformatics. 2006;7(1). doi: 10.1186/1471-2105-7-41.
  • [6] Orzechowski P, Boryczko K. Text Mining with Hybrid Biclustering Algorithms. Lecture Notes in Computer Science. 2016;9693:102-113. doi:10.1007/978-3-319-39384-1_9.
  • [7] Latkowski R. On Decomposition for Incomplete Data. Fundamenta Informaticae. 2003;54:1-16.
  • [8] Busygin S, Prokopyev O, Pardalos PM. Biclustering in data mining. Computers & Operations Research. 2008;35(9):2964-2987. URL https://doi.org/10.1016/j.cor.2007.01.005.
  • [9] Ignatov DI, Watson BW. Towards a Unified Taxonomy of Biclustering Methods. CEUR Workshop Proceedings. 2016;1522:23-39.
  • [10] Serin A, Vingron M. DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach. Algorithms for Molecular Biology. 2011;6(1). URL https://doi.org/10.1186/1748-7188-6-18.
  • [11] Chikalov I, Lozin VV, Lozina I, Moshkov M. Three Approaches to Data Analysis - Test Theory, Rough Sets and Logical Analysis of Data. vol. 41 of Intelligent Systems Reference Library. Springer; 2013. doi:10.1007/978-3-642-28667-4.
  • [12] Tanay A, Sharan R, Shamir R. Discovering statistically significant biclusters in gene expression data. Bioinformatics. 2002;18(suppl 1):S136.
  • [13] Bergmann S, Ihmels J, Barkai N. Iterative signature algorithm for the analysis of large-scale gene expression data. Phys Rev E. 2003;67:031902. doi:10.1103/PhysRevE.67.031902.
  • [14] Murali TM, Kasif S. Extracting Conserved Gene Expression Motifs from Gene Expression Data. Pacific Symposium on Biocomputing. 2003;8:77-88. URL https://doi.org/10.1142/9789812776303_0008.
  • [15] Ben-Dor A, Chor B, Karp R, Yakhini Z. Discovering Local Structure in Gene Expression Data: The Order-preserving Submatrix Problem. Journal of Computational Biology. 2003;3-4(10):373-384. URL https://doi.org/10.1089/10665270360688075.
  • [16] Hochreiter S, Bodenhofer U, Heusel M, Mayr A, Mitterecker A, Kasim A. FABIA: factor analysis for bicluster acquisition. Bioinformatics. 2010;26(12):1520-1527. doi: 10.1093/bioinformatics/btq227.
  • [17] Prelić A, Bleuler S, Zimmermann P, Wille A, Gruissem W. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics. 2006;22(9):1122-1129. doi:10.1093/bioinformatics/btl060.
  • [18] Orzechowski P, Boryczko K. Propagation-based biclustering algorithm for extracting inclusion-maximal motifs. Computing and Informatics. 2016;35(2):391-410.
  • [19] Michalak M, Stawarz M. Generating and Postprocessing of Biclusters from Discrete Value Matrices. Lecture Notes in Computer Science. 2011;6922:103-112. doi:10.1007/978-3-642-23935-9_10.
  • [20] Zhong N, Skowron A. A Rough Set-Based Knowledge Discovery Process. International Journal of Applied Mathematics and Computer Science. 2001;11:603-619.
  • [21] Ślęzak D, Janusz A. Ensembles of Bireducts: Towards Robust Classification and Simple Representation. Lecture Notes in Computer Science. 2011;7105:64-77. doi:10.1007/978-3-642-27142-7_9.
  • [22] Janusz A, Ślęzak D. Rough Set Methods for Attribute Clustering and Selection. Applied Artificial Intelligence. 2014;28(3):220-242. URL https://doi.org/10.1080/08839514.2014.883902.
  • [23] Michalak M, Dubiel M, Urbanek J. Application for Logical Expression Processing. Computer Science & Information Technology. 2016;6(65):1-9. doi:10.5121/csit.2016.60801.
  • [24] Brown FM. Boolean Reasoning. Springer US; 1990.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5c7da09c-4a1b-477b-9799-9d8820437cc7
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