Several problem in Artificial Intelligence and Pattern Recognition are computationally intractable due to their inherent complexity and the exponential size of the solution space. One example of such problems is biclustering, a specific clustering problem where rows and columns of a data-matrix must be clustered simultaneously. Quantum information processing could provide a viable alternative t...

Mrvar and Doreian recently defined a notion of bipartite clustering in bipartite signed graphs that gives a measure of imbalance of the signed graph, different from previous measures (the “frustration index” or “line index of balance”, l, and Davis’s clusterability). A biclustering of a bipartite signed graph is a pair (π1, π2) of partitions of the two color classes; the sets of the partitions ...

As we have seen in previous lectures, the technology of DNA chips allows the measurement of mRNA levels simultaneously for thousands of genes. The abundance of readily available data allows us the option to try to extract information, not easily seen while analyzing a single test, from the results of a multitude of tests. The results of DNA chip experiments are usually organized together in a g...

In this paper, we present a new biclustering algorithm to provide the geometrical interpretation of similar microarray gene expression profiles. Different from standard clustering analyses, biclustering methodology can perform simultaneous classification on the row and column dimensions of a data matrix. The main object of the strategy is to reveal the submatrix, in which a subset of genes exhi...

Biclustering is a very useful data mining technique which identifies coherent patterns from microarray gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. Biclustering is a powerful analytical tool for the biologist and has generated considerable interest over the past few decades. Many biclu...

There are subsets of genes that have similar behavior under subsets of conditions, so we say that they coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of utmost importance to make a simultaneous clustering of genes and conditions to ide...

Sparse 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 the left- and right-singular vectors to be sparse...

As we have seen in previous lectures, the technology of DNA chips allows the measurement of mRNA levels simultaneously for thousands of genes. The results of DNA chip experiments are usually organized together in a gene expression matrix, with rows corresponding to genes and columns corresponding to conditions. A bicluster can be defined as a submatrix spanned by a subset of genes and a subset ...

For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a datas...

Biclustering has the potential to make significant contributions in the fields of information retrieval, web mining, and so forth. In this paper, the authors analyze the complex association between users and pages of a web site by using a biclustering algorithm. This method automatically identifies the groups of users that show similar browsing patterns under a specific subset of the pages. In ...