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Title of Journal: Int J Mach Learn Cyber

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Abbravation: International Journal of Machine Learning and Cybernetics

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Springer Berlin Heidelberg

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DOI

10.1007/s00703-014-0361-1

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1868-808X

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Concept lattice compression in incomplete contexts

Authors: Caiping Li Jinhai Li Miao He
Publish Date: 2014/07/27
Volume: 7, Issue: 4, Pages: 539-552
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Abstract

Incomplete contexts are a kind of formal contexts in which information about the relationship between some objects and attributes is not available or is lost Knowledge discovery in incomplete contexts is of interest because such databases are frequently encountered in the real world The existing work has proposed an approach to construct the approximate concept lattice of an incomplete context Generally speaking however the huge nodes in the approximate concept lattice make the obtained conceptual knowledge difficult to be understood and weaken the efficiency of the related decisionmaking analysis as well Motivated by this problem this paper puts forward a method to compress the approximate concept lattice using Kmedoids clustering To be more concrete firstly we discuss the accuracy measure of approximate concepts in incomplete contexts Secondly the similarity measure between approximate concepts is presented via the importance degrees of an object and an attribute And then the approximate concepts of an incomplete context are clustered by means of Kmedoids clustering Moreover we define the socalled Kdeletion transformation to achieve the task of compressing the approximate concept lattice Finally we conduct some experiments to perform a robustness analysis of the proposed clustering method with respect to the parameters varepsilon and K and show the average rate of compression of approximate concept latticeThe authors would like to thank EditorinChief and three anonymous reviewers for their valuable comments and helpful suggestions which lead to a significant improvement on the manuscript This work was supported by the National Natural Science Foundation of China Nos 61305057 and 11371014 and the Natural Science Research Foundation of Kunming University of Science and Technology No 14118760

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