Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A partition-based approach towards constructing Galois (concept) lattices
Discrete Mathematics
Building Concept (Galois) Lattices from Parts: Generalizing the Incremental Methods
ICCS '01 Proceedings of the 9th International Conference on Conceptual Structures: Broadening the Base
Towards a machine learning approach based on incremental concept formation
Intelligent Data Analysis
Journal of Biomedical Informatics
Two FCA-Based Methods for Mining Gene Expression Data
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Analyzing Social Networks Using FCA: Complexity Aspects
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Minimizing the expected complete influence time of a social network
Information Sciences: an International Journal
Research on a union algorithm of multiple concept lattices
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
Research on domain ontology in different granulations based on concept lattice
Knowledge-Based Systems
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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.