Wyniki wyszukiwania - BazTech

1

Heuristic search of exact biclusters in binary data

Michalak Marcin, Jaksik Roman, Ślęzak Dominik

International Journal of Applied Mathematics and Computer Science

|

2020

|

Vol. 30, no. 1

161--171

EN

The biclustering of two-dimensional homogeneous data consists in finding a subset of rows and a subset of columns whose intersection provides a set of cells whose values fulfil a specified condition. Usually it is defined as equality or comparability. One of the presented approaches is based on the model of Boolean reasoning, in which finding biclusters in binary or discrete data comes down to the problem of finding prime implicants of some Boolean function. Due to the high computational complexity of this task, the application of some heuristics should be considered. In the paper, a modification of the well-known Johnson strategy for prime implicant approximation induction is presented, which is necessary for the biclustering problem. The new method is applied to artificial and biomedical datasets.

2

Boolean Representation for Exact Biclustering

Michalak M., Ślęzak D.

Fundamenta Informaticae

|

2018

|

Vol. 161, nr 3

275--297

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.

3

Gait patterns classification based on cluster and bicluster analysis

Pauk J., Minta-Bielecka K.

Biocybernetics and Biomedical Engineering

|

2016

|

Vol. 36, no. 2

391--396

EN

Gait patterns of hemiplegia patients have many potential applications such as assistance in diagnosis or clinical decision-making. Many techniques were developed to classify gait patterns in past years; however, these methods have some limitations. The main goal of the study was to present the performance evaluation results of the new biclustering algorithm called KMB. The second objective was to compare clustering and biclustering methods. The study was performed based on the gait patterns of 41 hemiplegia patients over 12 months post-stroke, at the age of 48.6 ± 19.6 years. Spatial–temporal gait parameters and joint moments were measured using motion capture system and force plates. Clustering and biclustering algorithms were applied for data consisting of joint moments of lower limbs. The obtained results of this study based on joint moments, clustering, and biclustering can be applied to evaluate patient condition and treatment effectiveness. We suggest that the biclustering algorithm compared to clustering algorithms better characterizes the specific traits and abnormalities of the joint moments, especially in case of hemiplegia patients.

4

Effective biclustering on GPU - capabilities and constraints

Orzechowski P., Boryczko K.

Przegląd Elektrotechniczny

|

2015

|

R. 91, nr 8

131--134

PL

W artykule przedstawiono korzyści i ograniczenia związane z projektowaniem równoległego algorytmu biklasteryzacji, przeznaczonego na GPU. Zaprezentowano definicję biklasteryzacji oraz skrótowo opisano architekturze GPU. Zestawiono popularne wzorce strategii implementacji algorytmów, przydatne w projektowaniu efektywnych rozwiązań na GPU. Publikacja zawiera także praktyczne wskazówki programistyczne, w kontekście implementacji algorytmów biklasteryzacji w języku CUDA/OpenCL.

EN

This article presents the benefits and limitations related to designing a parallel biclustering algorithm on a GPU. A definition of biclustering is provided together with a brief description of the GPU architecture. We then review algorithm strategy patterns, which are helpful in providing efficient implementations on GPU. Finally, we highlight programming aspects of implementing biclustering algorithms in CUDA/OpenCL programming language.

5

Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets

Orzechowski P., Boryczko K.

Bio-Algorithms and Med-Systems

|

2015

|

Vol. 11, no. 4

243--248

EN

Parallel computing architectures are proven to significantly shorten computation time for different clustering algorithms. Nonetheless, some characteristics of the architecture limit the application of graphics processing units (GPUs) for biclustering task, whose function is to find focal similarities within the data. This might be one of the reasons why there have not been many biclustering algorithms proposed so far. In this article, we verify if there is any potential for application of complex biclustering calculations (CPU+GPU). We introduce minimax with Pearson correlation – a complex biclustering method. The algorithm utilizes Pearson’s correlation to determine similarity between rows of input matrix. We present two implementations of the algorithm, sequential and parallel, which are dedicated for heterogeneous environments. We verify the weak scaling efficiency to assess if a heterogeneous architecture may successfully shorten heavy biclustering computation time.