Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
The semantic web: yet another hip?
Data & Knowledge Engineering - DKE 40
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Formal Concept Analysis in Software Engineering
Proceedings of the 26th International Conference on Software Engineering
A local approach to concept generation
Annals of Mathematics and Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Concept similarity in Formal Concept Analysis: An information content approach
Knowledge-Based Systems
Understanding Social Networks Using Formal Concept Analysis
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Formal concept analysis in information science
Annual Review of Information Science and Technology
Shifting concepts to their associative concepts via bridges
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Formal concept analysis (FCA) has been applied successively in diverse fields such as data mining, conceptual modeling, social networks, software engineering, and the semantic web. One shortcoming of FCA, however, is the large number of concepts that typically arise in dense datasets hindering typical tasks such as rule generation and visualization. To overcome this shortcoming, it is important to develop formalisms and methods to segment, categorize and cluster formal concepts. The first step in achieving these aims is to define suitable similarity and dissimilarity measures of formal concepts. In this paper we propose three similarity measures based on existent set-based measures in addition to developing the completely novel zeros-induced measure. Moreover, we formally prove that all the measures proposed are indeed similarity measures and investigate the computational complexity of computing them. Finally, an extensive empirical evaluation on real-world data is presented in which the utility and character of each similarity measure is tested and evaluated.