Biclustering, block clustering,[1][2] Co-clustering or two-mode clustering[3][4][5] is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. The term was first introduced by Boris Mirkin[6] to name a technique introduced many years earlier,[6] in 1972, by John A. Hartigan.[7]
Given a set of samples represented by an -dimensional feature vector, the entire dataset can be represented as rows in columns (i.e., an matrix). The Biclustering algorithm generates Biclusters. A Bicluster is a subset of rows which exhibit similar behavior across a subset of columns, or vice versa.
- ^ G. Govaert; M. Nadif (2008). "Block clustering with bernoulli mixture models: Comparison of different approaches". Computational Statistics and Data Analysis. 52 (6): 3233–3245. doi:10.1016/j.csda.2007.09.007.
- ^ R. Balamurugan; A.M. Natarajan; K. Premalatha (2015). "Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data". Applied Artificial Intelligence. 29 (4): 353–381. doi:10.1080/08839514.2015.1016391. S2CID 44624424.
- ^ G. Govaert; M. Nadif (2013). Co-clustering: models, algorithms and applications. ISTE, Wiley. ISBN 978-1-84821-473-6.
- ^ R. Balamurugan; A.M. Natarajan; K. Premalatha (2016). "A Modified Harmony Search Method for Biclustering Microarray Gene Expression Data". International Journal of Data Mining and Bioinformatics. 16 (4): 269–289. doi:10.1504/IJDMB.2016.082205.
- ^ Van Mechelen I, Bock HH, De Boeck P (2004). "Two-mode clustering methods:a structured overview". Statistical Methods in Medical Research. 13 (5): 363–94. CiteSeerX 10.1.1.706.4201. doi:10.1191/0962280204sm373ra. PMID 15516031. S2CID 19058237.
- ^ a b Mirkin, Boris (1996). Mathematical Classification and Clustering. Kluwer Academic Publishers. ISBN 978-0-7923-4159-8.
- ^ Hartigan JA (1972). "Direct clustering of a data matrix". Journal of the American Statistical Association. 67 (337): 123–9. doi:10.2307/2284710. JSTOR 2284710.