Paper Info
Title
Towards a Unified Taxonomy of Biclustering Methods.
Abstract
Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used to find genes, which are active in a subset of situations, thus being candidates for biomarkers. However, the authors of those biclustering techniques that are popular in gene expression analysis, may overlook the existing methods. For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts. Moreover, even if bioinformatics classify the biclustering methods according to reasonable domain-driven criteria, their classification taxonomies may be different from survey to survey and not full as well. So, in this paper we propose to use concept lattices as a tool for taxonomy building (in the biclustering domain) and attribute exploration as means for cross-domain taxonomy completion.
Year
Venue
Field
2017
arXiv: Artificial Intelligence
Data mining,Computer science,Homogeneous,Unsupervised learning,Artificial intelligence,Biclustering,Cluster analysis,Machine learning
DocType
Volume
ISSN
Journal
abs/1702.05376
Russian and South African Workshop on Knowledge Discovery Techniques Based on Formal Concept Analysis (RuZA 2015), November 30 - December 5, 2015, Stellenbosch, South Africa, In CEUR Workshop Proceedings, Vol. 1552, p. 23-39
Citations 
PageRank 
References 
1
0.34
24
Authors
2
Name
Order
Citations
PageRank
Dmitry I. Ignatov123929.53
Bruce W. Watson233853.24