Title: A bottom-up algorithm of vertical assembling concept lattices

Authors: Lei Zhang; Hongli Zhang; Xiajiong Shen; Lihua Yin

Addresses: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China; Institute of Data and Knowledge Engineering, Henan University, Kaifeng, Henan, 475004, China ' School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China ' Institute of Data and Knowledge Engineering, Henan University, Kaifeng, Henan, 475004, China ' Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

Abstract: One of the challenges in microarray data analysis is to interpret observed changes in terms of biological properties and relationships from massive amounts of gene expression data. As a powerful clustering tool, formal concept analysis has been used for making associations of gene expression clusters. The method of formal concept analysis constructs a concept lattice from the experimental data together with additional biological information. However, the time taken for constructing a concept lattice will rise sharply when the numbers of both gene clusters and properties are very large. In this article, we present an algorithm for assembling concept lattices for the parallel constructing concept lattice. The process of assembling two lattices is as follows. By traversing the diagram graph in a bottom-up fashion, all concepts in one lattice are added incremental into another sub-lattice one by one. In the process of adding a concept, the algorithm uses the diagram graph to find the generator concepts. It works only with the new and updated concepts of the concept which is added in the last time. The test results show that this algorithm outperforms other similar algorithms found in related literatures.

Keywords: formal concept analysis; concept lattices; vertical assembling; microarrays; bioinformatics; gene expression data; clustering analysis.

DOI: 10.1504/IJDMB.2013.053311

International Journal of Data Mining and Bioinformatics, 2013 Vol.7 No.3, pp.229 - 244

Received: 25 Jun 2012
Accepted: 25 Jun 2012
Published online: 07 Jun 2013 *

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