Boris Mirkin

Publications¹⁰³

• Article Nascimento S., Martins A., Relvas P., Luís J., Mirkin B. Core–shell clustering approach for detection and analysis of coastal upwelling // Computers & Geosciences. 2023. Vol. 179. Article 105421. doi
• Article Nascimento S., Martins A., Relvas P., Luis J., Boris Mirkin. Piece‐wise constant cluster modelling of dynamics of upwelling patterns // Expert Systems: The Journal of Knowledge Engineering. 2023. P. 1-16. doi
• Article Mirkin B., Shalileh S. Community Detection in Feature-Rich Networks Using Data Recovery Approach // Journal of Classification. 2022. Vol. 39. P. 432-462. doi
• Article Shalileh S., Mirkin B. Community Partitioning over Feature-Rich Networks Using an Extended K-Means Method // Entropy. 2022. Vol. 24. No. 5. Article 626. doi
• Chapter Zhirayr Hayrapetyan, Nascimento S., Trevor F., Dmitry Frolov, Boris Mirkin. Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion, in: Information Systems and Technologies: WorldCIST 2022, Volume 2 Issue 469. Springer, 2022. doi P. 141-147. doi
• Book Nascimento S., Martins A., Relvas P., Luís, J., Mirkin B. Progress in Artificial Intelligence: 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31–September 2, 2022, Proceedings / Рук.: S. Nascimento. Vol. 13566. Lisbon : Springer Publishing Company, 2022. doi
• Chapter Shalileh S., Mirkin B. A Method for Community Detection in Networks with Mixed Scale Features at Its Nodes, in: Complex Networks & Their Applications IX. Volume 1: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. Springer, 2021. P. 3-14. doi
• Chapter Mirkin B. A Method for Community Detection in Networks with Mixed Scale Features at its Nodes, in: Complex Networks & Their Applications IX. Volume 1: Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. Springer, 2021. P. 3-14. doi (in press)
• Chapter Shalileh S., Mirkin B. An Extension of K-Means for Least-Squares Community Detection in Feature-Rich Networks, in: COMPLEX NETWORKS 2021: Complex Networks & Their Applications X.. Springer, 2021. P. 285-296. doi (in press)
• Chapter Shalileh S., Mirkin B. Community detection in feature-rich networks to meet K-means, in: ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Association for Computing Machinery (ACM), 2021. P. 138-142. doi (in press)
• Chapter Shalileh S., Mirkin B. Detecting Communities in Feature-Rich Networks with a K-Means Method, in: Intelligent Data Engineering and Automated Learning – IDEAL 2021. Springer, 2021. P. 539-547. doi (in press)
• Article Shalileh S., Mirkin B. Least-squares community extraction in feature-rich networks using similarity data // Plos One. 2021. Vol. 16. No. 7. Article 0254377. doi
• Article Shalileh S., Mirkin B. Summable and nonsummable data‐driven models for community detection in feature‐rich networks // Social Network Analysis and Mining. 2021. Vol. 11. No. 1. P. 1-23. doi
• Chapter Mirkin B. A Data Recovery Method for Community Detection in Feature-Rich Networks, in: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery (ACM), 2020. doi Ch. 05. P. 99-105. (in press)
• Chapter Shalileh S., Mirkin B. A Data Recovery Method for Community Detection in Feature-Rich Networks, in: 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery (ACM), 2020. doi P. 99-104. doi
• Chapter Mirkin B., Frolov D., Vlasov A., Nascimento S., Fenner T. A Hybrid Approach to Interpretable Analysis of Research Paper Collections, in: WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics. Association for Computing Machinery (ACM), 2020. doi P. 184-189. doi
• Chapter Mirkin B., Frolov D., Vlasov A., Nascimento S., Fenner T. A Hybrid Approach to the Analysis of a Collection of Research Papers, in: Intelligent Data Engineering and Automated Learning – IDEAL 2020/ 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II Vol. 12490: Lecture Notes in Computer Science. Cham : Springer, 2020. P. 423-433. doi
• Chapter Frolov D., Taran Z., Mirkin B. A Method for Audience Extending in Programmatic Advertising by Using Parsimonious Generalization of User Segments, in: International Conference on Human Interaction and Emerging Technologies. Springer, 2020. doi P. 837-841. doi
• Chapter Shalileh S., Mirkin B. A One-by-One Method for Community Detection in Attributed Networks, in: Intelligent Data Engineering and Automated Learning – IDEAL 2020/ 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part II Vol. 12490: Lecture Notes in Computer Science. Cham : Springer, 2020. P. 413-422. doi
• Article de Amorim R. C., Makarenkov V., Mirkin B. Core clustering as a tool for tackling noise in cluster labels // Journal of Classification. 2020. Vol. 37. No. 1. P. 143-157. doi
• Chapter Shalileh S., Mirkin B. Detection of an unspecified number of communities in feature-rich networks, in: Proceedings of MARAMI 2020 - Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques - The 11th Conference on Network Modeling and Analysis(Vol-2750) Vol. Vol-2750: Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques - Network Modeling and Analysis 2020. CEUR-WS.org, 2020. P. 1-12.
• Article Taran Z., Mirkin B. Exploring patterns of corporate social responsibility using a complementary K-means clustering criterion // Business Research. 2020. Vol. 13. No. 2. P. 513-540. doi
• Chapter Frolov D., Mirkin B., Nascimento S., Fenner T. Globally Optimal Parsimoniously Lifting a Fuzzy Query Set Over a Taxonomy Tree, in: Optimization of Complex Systems: Theory, Models, Algorithms and Applications. Switzerland : Springer Publishing Company, 2020. doi P. 779-789. doi
• Article Frolov D., Nascimento S., Fenner T., Mirkin B. Parsimonious Generalization of Fuzzy Thematic Sets in Taxonomies Applied to the Analysis of Tendencies of Research in Data Science // Information Sciences. 2020. Vol. 512. P. 595-615. doi
• Chapter Frolov D., Mirkin B., Nascimento S., Fenner T., Taran Z. Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences, in: Intelligent Data Engineering and Automated Learning – IDEAL 2019 Vol. 2. Cham Switzerland: Springer, 2019. doi P. 3-11. doi
• Article Mirkin B., Fenner T. Distance and Consensus for Preference Relations Corresponding to Ordered Partitions // Journal of Classification. 2019. Vol. 36. No. 2. P. 350-367. doi
• Chapter Frolov D., Mirkin B., Nascimento S., Fenner T. Method for Generalization of Fuzzy Sets, in: International Conference on Artificial Intelligence and Soft Computing. 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings Part 1. Issue 11508. Cham : Springer, 2019. doi P. 273-286. doi
• Chapter Myachin A. L., Mirkin B. Ordinal Equivalence Classes for Parallel Coordinates, in: Intelligent Data Enginneering and Automated Learning - IDEAL2019 Vol. 2. Springer, 2019. doi P. 525-533. doi
• Chapter Frolov D., Mirkin B., Nascimento S., Fenner T. Using Domain Taxonomy to Model Generalization of Thematic Fuzzy Clusters, in: CONTENT 2019, The Eleventh International Conference on Creative Content Technologies. International Academy, Research, and Industry Association (IARIA), 2019. P. 20-25.
• Chapter Frolov D., Mirkin B., Nascimento S., Fenner T. Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster, in: Fuzzy Systems (FUZZ-IEEE), IEEE International Conference Proceedings. IEEE, 2019. P. 1-6. doi
• Preprint Mirkin B., Frolov D., Fenner T., Nascimento S. Finding an appropriate generalization for a fuzzy thematic set in taxonomy / Издательский дом ВШЭ. Series WP7 "Математические методы анализа решений в экономике, бизнесе и политике". 2018. No. 4.
• Article Murtagh F., Orlov M. A., Mirkin B. Qualitative Judgement of Research Impact: Domain Taxonomy as a Fundamental Framework for Judgement of the Quality of Research // Journal of Classification. 2018. Vol. 35. No. 1. P. 5-28. doi
• Chapter Nascimento S., Mirkin B. Applying Anomalous Cluster Approach to Spatial Clustering, in: Uncertainty Modeling. Dedicated to Professor Boris Kovalerchuk on his Anniversary Issue 683. Springer, 2017. doi P. 147-157. doi
• Chapter Nascimento S., Mirkin B. Ideal type model and an associated method for relational fuzzy clustering, in: 2017 IEEE International Conference Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. doi P. 1-6. doi
• Book Родин И. В., Mirkin B. International Joint Conference on Rough Sets, Springer, Cham.. Springer, 2017.
• Article Mirkin B., Amorim R., Makarenkov V., Shestakoff A. The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning // Pattern Recognition. 2017. Vol. 67. P. 62-72.
• Chapter Бочаров А. А., Gnatyshak D. V., Ignatov D. I., Mirkin B., Shestakoff A. A Lattice-based Consensus Clustering Algorithm, in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings / Ed. by M. Huchard, S. Kuznetsov. Vol. 1624. M. : Higher School of Economics, National Research University, 2016. P. 45-56.
• Article de Amorim R. C., Makarenkov V., Mirkin B. A-Wardpβ: Effective hierarchical clustering using the Minkowski metric and a fast k-means initialisation // Information Sciences. 2016. Vol. 370-371. No. November . P. 343-354. doi
• Chapter Mirkin B. Approximate clusters, biclusters and n-clusters in the analysis of binary and general data matrices, in: CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings / Ed. by M. Huchard, S. Kuznetsov. Vol. 1624. M. : Higher School of Economics, National Research University, 2016. P. 7-8.
• Preprint Mirkin B., Fenner T. TIED RANKINGS, ORDERED PARTITIONS, AND WEAK ORDERS: DISTANCE AND CONSENSUS / Издательский дом ВШЭ. Series WP7 "Математические методы анализа решений в экономике, бизнесе и политике". 2016. No. 8.
• Chapter Родин И. В., Artemova E., Dubov M., Mirkin B. Visualization of Dynamic Reference Graphs, in: Proceedings of TextGraphs-10: the Workshop on Graph-based Methods for Natural Language Processing. Stroudsburg, PA : Association for Computational Linguistics, 2016. P. 34-38.
• Article Nascimento S., Casca S., Mirkin B. A seed expanding cluster algorithm for deriving upwelling areas on sea surface temperature images // Computers & Geosciences. 2015. Vol. 85. No. Part B. P. 74-85. doi
• Article Mirkin B., Kovaleva E. V. Bisecting K-Means and 1D Projection divisive clustering: a unified framework and experimental comparison // Journal of Classification. 2015. Vol. 32. No. 3. P. 414-442. doi
• Article Artemova E., Mirkin B. Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources // Annals of Data Science. 2015. Vol. 2. No. 1. P. 61-82.
• Chapter Mirkin B., Orlov M. A. Three Aspects of the Research Impact by a Scientist: Measurement Methods and an Empirical Evaluation, in: Optimization, Control, and Applications in the Information Age: In Honor of Panos M. Pardalos's 60th Birthday Vol. 130: Springer Proceedings in Mathematics & Statistics. Switzerland : Springer, 2015. doi P. 233-259. doi
• Article Ignatov D. I., Gnatyshak D. V., Sergei O. Kuznetsov, Boris G. Mirkin. Triadic Formal Concept Analysis and triclustering: searching for optimal patterns // Machine Learning. 2015. Vol. 101. No. 1. P. 271-302. doi
• Chapter Artemova E. A Method for Refining a Taxonomy by Using Annotated Suffix Trees and Wikipedia Resources, in: Procedia Computer Science. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. National Research University Higher School of Economics (HSE) in Moscow (Russia) on June 3-5, 2014 / Ed. by Y. Shi, A. Lepskiy, F. T. Aleskerov. Vol. 31. Amsterdam : Elsevier, 2014. Ch. 22. P. 193-200.
• Chapter Mirkin B., Shestakoff A. A Note on the Effectiveness of the Least Squares Consensus Clustering, in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday / Ed. by F. T. Aleskerov, B. I. Goldengorin, P. M. Pardalos. Vol. 92. Berlin : Springer, 2014.
• Chapter Mirkin B., Artemova E. An AST method for scoring string-to-text similiarity in semantic text analysis, in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday / Ed. by F. T. Aleskerov, B. I. Goldengorin, P. M. Pardalos. Vol. 92. Berlin : Springer, 2014.
• Chapter E. Morenko, Artemova E., Mirkin B. Conceptual maps: construction over a text collection and analysis, in: Analysis of Images, Social Networks and Texts Third International Conference, AIST 2014, Yekaterinburg, Russia, April 10-12, 2014, Revised Selected Papers / Ed. by D. I. Ignatov, M. Y. Khachay, A. Panchenko, N. Konstantinova, R. Yavorskiy. Vol. 439. Berlin : Springer, 2014. P. 163-169.
• Chapter Mirkin B., Andrey Shestakov. Least-squares consensus clustering versus: (a) other consensus approaches and (b) k-means, in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday / Ed. by F. T. Aleskerov, B. I. Goldengorin, P. M. Pardalos. Vol. 92. Berlin : Springer, 2014. (in press)
• Preprint Mirkin B., Orlov M. A. RESEARCH IMPACT: LEVEL OF RESULTS, CITATION, MERIT / Издательский дом ВШЭ. Series WP7 "Математические методы анализа решений в экономике, бизнесе и политике". 2014. No. 9.
• Article Mirkin B. Review of “Cell formation in industrial engineering: theory, algorithms and experiments” by Boris Goldengorin, Dmitry Krushinsky, Panos M. Pardalos // Optimization Letters. 2014
• Chapter Mirkin B., Amorim R. Selecting the Minkowski Exponent for Intelligent K-Means with Feature Weighting, in: Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday / Ed. by F. T. Aleskerov, B. I. Goldengorin, P. M. Pardalos. Vol. 92. Berlin : Springer, 2014. P. 1250-1257. doi
• Chapter Artemova E., Mirkin B. Computationally refining a taxonomy by using annotated suffix trees over Wikipedia resources, in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (Бекасово, 29 мая - 2 июня 2013 г.). В 2-х т. Т. 2: Доклады специальных секций. Вып. 12(19). М. : РГГУ, 2013. P. 177-185.
• Chapter Mirkin B. Individual approximate clusters: methods, properties, applications, in: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing Issue 8170: Lecture Notes in Artificial Intelligence. Heidelberg : Springer, 2013. P. 26-37.
• Article Mirkin B., Nascimento S., Felizardo R. Laplacian normalization for deriving thematic fuzzy clusters with an additive spectral approach // Expert Systems: The Journal of Knowledge Engineering. 2013. Vol. 30. No. 4. P. 294-305.
• Chapter Mirkin B., Shestakoff A. Least squares consensus clustering: criteria, methods, experiments, in: Advances in Information Retrieval. L. : Springer, 2013. P. 764-768.
• Chapter Mirkin B., Svetlova L., Lei H. MFWK-Means: Minkowski Metric Fuzzy Weighted K-Means for high dimensional data clustering, in: Information Reuse and Integration (Proceedings of the 2013 IEEE 14th International Conference on Information Reuse and Integration). Piscataway : IEEE, 2013. P. 692-699.
• Chapter Boris Mirkin. Methods for interpretation of data in medical informatics, in: Advances in intelligent analysis of medical data and decision support systems Vol. 473: Studies in computational intelligence. Heidelberg : Springer, 2013. P. 15-22.
• Chapter Mirkin B. Summary and semi-average similarity criteria for individual clusters, in: Models, Algorithms, and Technologies for Network Analysis / Ed. by Boris I. Goldengorin, Valery A. Kalyagin, Panos M. Pardalos. Vol. 59. NY : Springer, 2013. P. 101-126.
• Article Mirkin B., Nascimento S. Additive spectral method for fuzzy cluster analysis of similarity data including community structure and affinity matrices // Information Sciences. 2012. No. 183. P. 16-34. doi
• Book Mirkin B. Clustering: A Data Recovery Approach. L. : CRC Press, 2012.
• Chapter Mirkin B., Nascimento S., Pereira L. M. Method for Intelligent Representation of Research Activities of an Organization over a Taxonomy of Its Field, in: Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms, Series on Intelligent Systems Reference Library Vol. 29: Series on Intelligent Systems Reference Library. Heidelberg : Springer, 2012. P. 423-454.
• Article Mirkin B., Amorim R. Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering // Pattern Recognition. 2012. Vol. 45. No. 3. P. 1061-1075. doi
• Chapter Artemova E., Чугунова О. Н., Аскарова Ю. А., Nascimento S., Mirkin B. Abstracting concepts from text documents by using an ontology, in: CDUD – 2010: International Workshop on Concept Discovery in Unstructured Data. M. : Higher School of Economics Publishing House, 2011. P. 20-31.
• Article Mirkin B. Approximate bicluster and tricluster boxes in the analysis of binary data // Lecture Notes in Computer Science. 2011. Vol. 6743. P. 248-256.
• Article Mirkin B. Choosing the number of clusters // WIREs Data Mining and Knowledge Discovery. 2011. No. 3. P. 252-260.
• Book Mirkin B. Core concepts in data analysis: summarization, correlation, visualization (Undergraduate topics in Computer Science). L. : Springer, 2011. doi
• Article Nascimento S., Mirkin B. Developing Additive Spectral Approach to Fuzzy Clustering // Lecture Notes in Computer Science. 2011. Vol. 6743. P. 273-277.
• Article Nascimento S., Fenner T., Felizardo R., Mirkin B. How to visualize a crisp or fuzzy topic set over a taxonomy // Lecture Notes in Computer Science. 2011. Vol. 6744. P. 3-12.
• Book Lecture Notes in Artificial Intelligence / Ed. by S. Kuznetsov, D. Slezak, D. H. Hepting, B. Mirkin. Vol. 6743: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. L., NY, Dordrecht, Heidelberg : Springer, 2011.
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  Book Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. Proceedings / Ed. by S. Kuznetsov, D. Slezak, D. H. Hepting, B. Mirkin. Vol. 6743. Berlin, Heidelberg : Springer, 2011.
• Article Mirkin B., Fenner T., Nascimento S., Pereira L. M. A Hybrid Cluster-Lift Method for the Analysis of Research Activities // Lecture Notes in Computer Science. 2010. Vol. 6076. No. 1. P. 152-161.
• Article Voulgaris Z., Mirkin B. Choosing a discernibility measure for reject-option of individual and multiple classifiers // International Journal of General Systems. 2010. Vol. 39. No. 8. P. 855-871. doi
• Article Chiang M., Mirkin B. Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads // Journal of Classification. 2010. Vol. 27. No. 1. P. 3-40. doi
• Article Mirkin B., Fenner T., Kellam P., Camargo R. Similarity Clustering of Proteins Using Substantive Knowledge and Reconstruction of Evolutionary Gene Histories in Herpesvirus // Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta). 2010. Vol. 125. No. 3-6. P. 569-581.
• Chapter Mirkin B., Nascimento S., Pereira L. M. Cluster-lift method for mapping research activities over a concept tree, in: Recent Advances in Machine Learning II. Берлин, Хайдельберг : Springer, 2009. P. 245-257.
• Article Stanforth R., Mirkin B., Kolossov E. A measure of domain of applicability for QSAR modelling based on intelligent K-Means clustering // QSAR and Combinatorial Science. 2007. Vol. 26. No. 7. P. 837-844.
• Article Pampapathi R., Levene M., Mirkin B. A suffix tree approach to anti-spam email filtering // Machine Learning. 2006. Vol. 65. P. 309-338.
• Article Mirkin B. Cluster Analysis for Researchers: A Book Review // Journal of Classification. 2005. No. 21(2)
• Article Миркин Б. Г., Muchnik I. Layered clusters of tightness set functions // Applied Mathematics Letters. 2004. № 15(3)
• Article Koonin E., Fenner T., Mirkin B., Galperin M. Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes // BMC Evolutionary Biology. 2003. Vol. 3. No. 2
• Article Mirkin B., Rogozin I., Koonin E. Evolution of eukaryotic gene repertoire and gene structure: discovering the unexpected dynamics of genome evolution // Cold Spring Harbor Symposia on Quantitative Biology. 2003. No. 68
• Article Mirkin B., Nascimento S., Moura F. Modeling proportional membership in fuzzy clustering // IEEE Transactions on Fuzzy Systems. 2003. No. 11(2)
• Article Mirkin B., Sorokin A., Wolf Y., Rogozin I., Koonin E. Remarkable conservation of intron positions and massive, lineage-specific intron loss and gain in eukaryotic evolution // Current Biology. 2003. No. 13
• Article Mirkin B., Muchnik I. Induced layered clusters, hereditary mappings, and convex geometries // Applied Mathematics Letters. 2002. No. 15(4)
• Article Mirkin B., Levin M. I., Bakaleinik E. Intelligent K-Means clustering in analysis of newspaper articles on bribing // Искусственный интеллект. 2002. No. 2. P. 365-373.
• Article Mirkin B. Eleven ways to look at the chi-squared coefficient for contingency tables // American Statistician. 2001. Vol. 55. No. 2. P. 111-120.
• Article Mirkin B. Concept learning and feature selection based on square-error clustering // Machine Learning. 1999. No. 35(1)
• Article Mirkin B. Least-squares structuring, clustering, and related computational Issues // Computer Journal. 1998. No. 41(8)
• Article Mirkin B., Muchnik I. Monotone linkage clustering and quasi-concave set functions // Applied Mathematics Letters. 1997. No. 10(4)
• Article Mirkin B. Clustering for contingency tables: Boxes and partitions // Statistics and Computing. 1996. No. 6
• Article Smith R. T., Mirkin B., Muchnik I. A biologically consistent model for comparing phylogeny trees // Journal of Computational Biology. 1995. No. 2(4)
• Article Mirkin B., Hubert L., Arabie P. Additive two-mode clustering: The error-variance approach revisited // Journal of Classification. 1995. No. 12(2)
• Article Mirkin B., Eulenstein O., Vingron M. Duplication-based measures of difference between gene and species trees // Journal of Computational Biology. 1995. No. 5(1)
• Article Roberts F., Mirkin B. Consensus functions and patterns in molecular sequences // Bulletin of Mathematical Biology. 1992. No. 55
• Article Mirkin B. Sequential fitting procedures for linear data aggregation model  // Journal of Classification. 1990. No. 7
• Article Mirkin B. Additive clustering and qualitative factor analysis methods for similarity matrices // Journal of Classification. 1987. No. 2
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  Book Mirkin B., Родин С. Н. Graphs and Genes Vol. 11. Heidelberg : Springer, 1984.
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  Book Mirkin B. Group Choice. New Jersey : Wiley-Interscience, 1979.
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  Book Миркин Б. Г., Родин С. Н. Графы и гены. М. : Наука, 1977.
• Article Mirkin B. Description of some relations on the set of real-line intervals // Journal of Mathematical Psychology. 1972. No. 9(2)