CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings

Vol. 1624. M. : Higher School of Economics, National Research University, 2016.

Under the general editorship: M. Huchard, Kuznetsov S.

The 13th International Conference on “Concept Lattices and Applications (CLA 2016)” was held at National Research University Higher School of Economics, Moscow, Russia from July 18 until July 22, 2016. The CLA conference, organized since 2002, aims to provide to everyone interested in Formal Concept Analysis and more generally in Concept Lattices or Galois Lattices, an advanced view on some of the last research trends and applications in this field. It also aims to bring together students, professors, researchers and engineers, involved in all aspects of the study of concept lattices, from theory to implementations and practical applications. As the diversity of the selected papers shows, there is a wide range of research directions, around data and knowledge processing, including data mining, knowledge discovery, knowledge representation, reasoning, pattern recognition, together with logic, algebra and lattice theory. The program of the conference includes four keynote talks given by the following distinguished researchers: Lev D. Beklemishev (Mathematical Institute of Russian Academy of Science, Moscow), J´erˆome Euzenat (INRIA Grenoble Rhˆone-Alpes), Bernhard Ganter (TU-Dresden), Boris G. Mirkin (National Research University Higher School of Economics, Moscow). This volume includes the selected papers and the abstracts of the invited talks. This year, 46 papers were submitted, from which 28 papers were accepted as regular papers. We would like to thank here the contributing authors for their valuable work, the members of the program committee and the external reviewers who analyzed the papers with care. All of them participated to the continuing quality and importance of CLA, highlighting its key role in the field. Then we would also like to thank the steering committee of CLA for giving us the occasion of leading this edition of CLA, the conference participants for their participation and support, and people in charge of the organization, especially Larisa I. Antropova, Ekaterina L. Chernyak, Dmitry I. Ignatov, Olga V. Maksimenkova, whose help was very precious in many occasions and that contributed to the success of the event. We would like to thank our sponsors, namely National Research University Higher School of Economics, ExactPro company, Russian Foundation for Basic Research. Finally, we also do not forget that the conference was managed (quite easily) with the Easychair system, for many tasks including paper submission, selection, and reviewing.

Chapters

Global Optimization in Learning with Important Data: an FCA-Based Approach

Kashnitsky Y., Kuznetsov S., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. Ch. 19 P. 189–202.

Nowadays decision tree learning is one of the most popular classification and regression techniques. Though decision trees are not accurate on their own, they make very good base learners for advanced tree-based methods such as random forests and gradient boosted trees. However, applying ensembles of trees deteriorates interpretability of the final model. Another problem is ...

Added: October 6, 2016

On Stability of Triadic Concepts

Kuznetsov S., Makhalova T., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 245–253.

Triadic concept analysis has become a popular research direction, since triadic relations give natural models of many data collections. In this paper we address the problem of selecting most interesting concepts by proposing triadic stability indices ...

Added: October 11, 2016

Pattern Structures for Treatment Optimization

Natalia V. Korepanova, Sergei O. Kuznetsov, , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 217–229.

A comparison of different treatment strategies does not always result in determining the best one for all patients, one needs to study subgroups of patients with significant difference in efficiency between treatment strategies. To solve this problem an approach to subgroups generation is proposed, where data are described in terms of a pattern structure and ...

Added: October 12, 2016

Building a Domain Knowledge Model based on a Concept Lattice Integrating Expert Constraints

Tang M. T., Buzmakov A. V., Toussaint Y. et al., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 349–362.

Traditionally, FCA can be used as a tool for eliciting from data a class schema in the form of either a set of attribute implications or a concept lattice. However, such a schema does not necessarily t the point of view of a domain expert for di erent reasons, e.g. noise, errors or exceptions in the data. For example, ...

Added: October 14, 2016

On Scaling of Fuzzy FCA to Pattern Structures?

Buzmakov A. V., Napoli A., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 85–96.

FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich stan- dard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we ...

Added: October 14, 2016

Unified External Data Access Implementation in Formal Concept Analysis Research Toolbox

Parinov A., Neznanov A., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 285–296.

Formal Concept Analysis (FCA) provides mathematical models, methods and algorithms for data analysis. However, by now there is no easily available program system, which would provide data analyst with unified, intelligible and transparent access to various external data sources with large amount of heterogeneous data for subsequent FCA-based knowledge discovery. The lack of such tools ...

Added: October 19, 2016

Continuous target variable prediction with augmented interval pattern structures: Lazy algorithm

Masyutin A., Kuznetsov S., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 273–284.

Pattern structures are known to provide a tool for predictive modeling and classification. However, in order to generate classification rules concept lattice should be built. This procedure may take much time and resources. In previous work it was shown that it is possible to escape the problem with so-called lazy associative classification algorithm. It does ...

Added: October 23, 2016

A Lattice-based Consensus Clustering Algorithm

Бочаров А. А., Gnatyshak D. V., Ignatov D. I. et al., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 45–56.

We propose a new algorithm for consensus clustering, FCA-Consensus, based on Formal Concept Analysis. As the input, the algorithm takes T partitions of a certain set of objects obtained by k-means algorithm after T runs from different initialisations. The resulting consensus partition is extracted from an antichain of the concept lattice built on a formal ...

Added: October 24, 2016

Approximate clusters, biclusters and n-clusters in the analysis of binary and general data matrices

Mirkin B., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 7–8.

Аpproximate cluster structures are those of formal concepts and n-concepts with added numerical intensity weights. The talk presents theoretical results and computational methods for approximate clustering and n-clustering as extensions of the algebraic-geometrical properties of numerical matrices (SVD and the like) to the situations where one or most of elements of the solutions to be ...

Added: March 3, 2017

Partial Duplication of Convex Sets in Lattices.

Beaudou L., Nourine L., , in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop ProceedingsVol. 1624.: M.: Higher School of Economics, National Research University, 2016. P. 33–44.

In this paper, we generalize the classical duplication of intervals in lattices. Namely, we deal with partial duplication instead of complete convex subsets. We characterize these subsets that guarantee the result to remain a lattice. ...

Added: April 11, 2019

Keywords: Formal Concept Analysis, concept lattices