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Pattern Structures for Risk Group Identification
P. 13-21.
Korepanova N., Kuznetsov S.
Today personalized medicine is one of the most popular interdisciplinary research field, risk group identification being one of its most important tasks. Even though the first attempts to estimate the effect of patient’s characteristics on the outcome were proposed in statistics in the middle of the twentieth century, it is still an open question how to explore such effects properly. In this paper we propose a trial version of the approach to risk group specification based on pattern structures and competing risk estimation, and discuss further steps of research on its performance and specificity.
Publication based on the results of:
Buzmakov A. V., Napoli A., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016). : M. : [б.и.], 2016. P. 89-96.
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 introduce a scaling procedure that ...
Added: October 14, 2016
Buzmakov A., Neznanov A., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013). Issue 1058.: Beijing : CEUR Workshop Proceedings, 2013. Ch. 7. P. 49-56.
A new general and efficient architecture for working with pattern structures, an extension of FCA for dealing with “complex” descriptions, is introduced and implemented in a subsystem of Formal Concept Analysis Research Toolbox (FCART). The architecture is universal in terms of possible dataset structures and formats, techniques of pattern structure manipulation. ...
Added: October 26, 2014
Kaytoue M., Codocedo V., Buzmakov A. V. et al., , in : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings. * III. Vol. 9286.: Dordrecht, L., Heidelberg, NY, Cham : Springer, 2015. P. 227-231.
This article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances are mainly published in artificial intelligence and FCA dedicated venues, a ...
Added: October 23, 2015
Buzmakov A. V., Kuznetsov S., Napoli A., , in : 2017 IEEE 17th International Conference on Data Mining (ICDM). : New Orleans : IEEE, 2017. Ch. 89. P. 757-762.
A scalable method for mining graph patterns stable under subsampling is proposed.
The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far.
We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns.
The ...
Added: September 26, 2017
Makhalova T., Ilvovsky D., Galitsky B., , in : ACL-IJCNLP 2015, Proceedings of the First Workshop on Computing News Storylines. : Beijing : [б.и.], 2015. P. 16-20.
A web search engine usually returns a long list of documents and it may be difficult for users to navigate through this collection
and find the most relevant ones. We present an approach to post-retrieval snippet clustering based on pattern structures construction on augmented syntactic parse trees. Since an algorithm may be too slow for a ...
Added: October 11, 2016
Kuznetsov S., Kaytoue M., Belfodil A., , in : International Journal of General Systems. Issue 49.: [б.и.], 2020. P. 271-285.
Order and lattice theory provides convenient mathematical tools for pattern mining, in particular for condensed irredundant representations of pattern spaces and their efficient generation. Formal Concept Analysis (FCA) offers a generic framework, called pattern structures, to formalize many types of patterns, such as itemsets, intervals, graphs, and sequence sets. Moreover, FCA provides generic algorithms to generate irredundantly all ...
Added: October 29, 2020
Goncharova E., Ilvovsky D., Galitsky B., , in : Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021). Vol. 2972.: CEUR-WS, 2021. P. 51-58.
Added: October 28, 2021
[б.и.], 2020
Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from practical and everyday computation. Its aim is to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies. All papers introducing or studying mathematical, logic and formal concepts and methods are welcome, provided that their motivation is ...
Added: October 29, 2020
Kashnitsky Y., Kuznetsov S., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016). : M. : [б.и.], 2016. P. 105-112.
Decision tree learning is one of the most popular classifica- tion techniques. However, by its nature it is a greedy approach to finding a classification hypothesis that optimizes some information-based crite- rion. It is very fast but may lead to finding suboptimal classification hy- potheses. Moreover, in spite of decision trees being easily interpretable, ensembles ...
Added: October 6, 2016
Gizdatullin D., Baixeries J., Ignatov D. I. et al., , in : Intelligent Data Processing 11th International Conference, IDP 2016, Barcelona, Spain, October 10–14, 2016, Revised Selected Papers. Vol. 794.: Switzerland : Springer, 2019. Ch. 6. P. 74-91.
There are many different methods for computing relevant
patterns in sequential data and interpreting the results. In this paper,
we compute emerging patterns (EP) in demographic sequences using
sequence-based pattern structures, along with different algorithmic solutions.
The purpose of this method is to meet the following domain
requirement: the obtained patterns must be (closed) frequent contiguous
prefixes of the input sequences. ...
Added: February 9, 2020
Leeuwenberg A., Buzmakov A. V., Toussaint Y. et al., , in : Formal Concept Analysis. 13th International Conference, ICFCA 2015, Nerja, Spain, June 23-26, 2015, Proceedings. Vol. 9113.: Springer, 2015. P. 153-168.
In this paper we explore the possibility of defining an original pattern structure for managing syntactic trees. More precisely, we are interested in the extraction of relations such as drug-drug interactions (DDIs) in medical texts where sentences are represented as syntactic trees. In this specific pattern structure, called STPS, the similarity operator is based on ...
Added: October 22, 2015
Alam M., Buzmakov A. V., Napoli A., Discrete Applied Mathematics 2018 Vol. 249 P. 2-17
With an increased interest in machine processable data and with the progress of semantic technologies, many datasets are now published in the form of RDF triples for constituting the so-called Web of Data. Data can be queried using SPARQL but there are still needs for integrating, classifying and exploring the data for data analysis and ...
Added: September 26, 2017
Belfodil A., Kuznetsov S., Kaytoue M., International Journal of General Systems 2020 Vol. 49 No. 8 P. 785-818
Order and lattice theory provides convenient mathematical tools for pattern mining, in particular for condensed irredundant representations of pattern spaces and their efficient generation. Formal Concept Analysis (FCA) offers a generic framework, called pattern structures, to formalize many types of patterns, such as itemsets, intervals, graphs, and sequence sets. Moreover, FCA provides generic algorithms to generate irredundantly all ...
Added: January 25, 2021
Kaytoue M., Napoli A., Kuznetsov S. et al., Information Sciences 2011 Vol. 181 No. 10 P. 1989-2001
This paper addresses the important problem of efficiently mining numerical data with formal concept analysis (FCA). Classically, the only way to apply FCA is to binarize the data, thanks to a so-called scaling procedure. This may either involve loss of information, or produce large and dense binary data known as hard to process. In the ...
Added: January 9, 2013
Buzmakov A. V., Egho E., Jay N. et al., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013). Issue 1058.: Beijing : CEUR Workshop Proceedings, 2013. P. 7-14.
In this paper, we are interested in the analysis of sequential data and we propose an original framework based on Formal Concept Analysis (FCA). For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Pattern structures are used in FCA for dealing with complex data such as ...
Added: October 23, 2015
Buzmakov A. V., Kuznetsov S., Napoli A., , in : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings. * 2. Vol. 9285.: Dordrecht, L., Cham, Heidelberg, NY : Springer, 2015. P. 157-172.
In pattern mining, the main challenge is the exponential explosion of the set of patterns. Typically, to solve this problem, a constraint for pattern selection is introduced. One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset. Frequency is an anti-monotonic function, i.e., given an infrequent pattern, ...
Added: October 22, 2015
Makhalova T., Ilvovsky D., Galitsky B., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2015). : Buenos Aires : [б.и.], 2015. P. 35-42.
Usually web search results are represented as long list of document snippets. It is
difficult for users to navigate through this collection of text. We propose clustering method
that uses pattern structure constructed on augmented syntactic parse trees. In addition, we
compare our method to other clustering methods and demonstrate the limitations of the
competitive methods. ...
Added: October 11, 2016
Kuznetsov S., Demko C., Bertet K. et al., , in : Electronic Procedings Theoretical Computer Science. Vol. 845.: [б.и.], 2020. P. 1-20.
In this article, we present a new data type agnostic algorithm calculating a concept lattice from heterogeneous and complex data. Our NextPriorityConcept algorithm is first introduced and proved in the binary case as an extension of Bordat's algorithm with the notion of strategies to select only some predecessors of each concept, avoiding the generation of ...
Added: October 29, 2020
Muratova A., Gizdatullin D., Ignatov D. I. et al., В кн. : Социология и общество: социальное неравенство и социальная справедливость (Екатеринбург , 19-21 октября 2016 года). Материалы V Всероссийского социологического конгресса. : М. : Российское общество социологов, 2016. С. 9601-9615.
In this paper, we summarize the results of recent studies on the application of pattern mining and machine learning to the analysis of demographic sequences. The main goal is the demonstration of demographers’ needs, including next-event prediction and the extraction of interesting patterns from substantial datasets of demographic data, which cannot be handled by conventional ...
Added: November 24, 2016
Natalia V. Korepanova, Sergei O. Kuznetsov, , in : 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. 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
Buzmakov A. V., Egho E., Jay N. et al., International Journal of General Systems 2016 Vol. 45 No. 2 P. 135-159
Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of “complex” sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of ...
Added: February 25, 2016
Kuznetsov S., , in : Proc. 11th International Conference on Formal Concept Analysis (ICFCA 2013). Vol. 7880.: Springer, 2013. P. 254-266.
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead ...
Added: June 2, 2013
Alam M., Buzmakov A. V., Codocedo V. et al., , in : Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015. : Palo Alto : AAAI Press, 2015. P. 823-829.
The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud. Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions. In this ...
Added: October 22, 2015
Ilya Semenkov, Sergei O. Kuznetsov, , in : Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021). Vol. 2972.: CEUR-WS, 2021. P. 105-112.
This paper presents different versions of classification ensemble methods based on pattern structures. Each of these methods is described and tested on multiple datasets (including datasets with exclusively numerical and exclusively nominal features). As a baseline model Random Forest generation is used. For some classification tasks the classification algorithms based on pattern structures showed better ...
Added: December 19, 2022