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Declarative Aspects in Explicative Data Mining for Computational Sensemaking

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Declarative Programming and Knowledge Management (WFLP 2017, WLP 2017, INAP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10997))

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Abstract

Computational sensemaking aims to develop methods and systems to “make sense” of complex data and information. The ultimate goal is then to provide insights and enhance understanding for supporting subsequent intelligent actions. Understandability and interpretability are key elements of that process as well as models and patterns captured therein. Here, declarativity helps to include guiding knowledge structures into the process, while explication provides interpretability, transparency, and explainability. This paper provides an overview of the key points and important developments in these areas, and outlines future potential and challenges.

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References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB, pp. 487–499. Morgan Kaufmann (1994)

    Google Scholar 

  2. Atzmueller, M.: Subgroup discovery. WIREs Data Min. Knowl. Discov. 5(1), 35–49 (2015)

    Article  Google Scholar 

  3. Atzmueller, M.: Onto explicative data mining: exploratory, interpretable and explainable analysis. In: Proceedings of Dutch-Belgian Database Day. TU Eindhoven, Netherlands (2017)

    Google Scholar 

  4. Atzmueller, M., Hayat, N., Schmidt, A., Klöpper, B.: Explanation-aware feature selection using symbolic time series abstraction: approaches and experiences in a petro-chemical production context. In: Proceedings of IEEE INDIN. IEEE Press, Boston (2017)

    Google Scholar 

  5. Atzmueller, M., Hayat, N., Trojahn, M., Kroll, D.: Explicative human activity recognition using adaptive association rule-based classification. In: Proceedings of IEEE International Conference on Future IoT Technologies. IEEE Press, Boston (2018, accepted)

    Google Scholar 

  6. Atzmueller, M., et al.: Big data analytics for proactive industrial decision support: approaches & first experiences in the context of the FEE project. atp edition 58(9) (2016)

    Article  Google Scholar 

  7. Atzmueller, M., Lemmerich, F.: A methodological approach for the effective modeling of Bayesian networks. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds.) KI 2008. LNCS (LNAI), vol. 5243, pp. 160–168. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85845-4_20

    Chapter  Google Scholar 

  8. Atzmueller, M., Puppe, F.: A methodological view on knowledge-intensive subgroup discovery. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 318–325. Springer, Heidelberg (2006). https://doi.org/10.1007/11891451_28

    Chapter  Google Scholar 

  9. Atzmueller, M., Puppe, F., Buscher, H.P.: Exploiting background knowledge for knowledge-intensive subgroup discovery. In: Proceedings of 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 647–652 (2005)

    Google Scholar 

  10. Atzmueller, M., Roth-Berghofer, T.: The mining and analysis continuum of explaining uncovered. In: Proceedings of AI (2010)

    Google Scholar 

  11. Atzmueller, M., Schmidt, A., Kloepper, B., Arnu, D.: HypGraphs: an approach for analysis and assessment of graph-based and sequential hypotheses. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z.W. (eds.) NFMCP 2016. LNCS (LNAI), vol. 10312, pp. 231–247. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61461-8_15

    Chapter  Google Scholar 

  12. Atzmueller, M., Seipel, D.: Using declarative specifications of domain knowledge for descriptive data mining. In: Seipel, D., Hanus, M., Wolf, A. (eds.) INAP/WLP-2007. LNCS (LNAI), vol. 5437, pp. 149–164. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00675-3_10

    Chapter  Google Scholar 

  13. Atzmueller, M., Sternberg, E.: Mixed-initiative feature engineering using knowledge graphs. In: Proceedings of K-CAP. ACM (2017)

    Google Scholar 

  14. Baumeister, J., Atzmüller, M., Puppe, F.: Inductive learning for case-based diagnosis with multiple faults. In: Craw, S., Preece, A. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 28–42. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46119-1_4

    Chapter  MATH  Google Scholar 

  15. Biran, O., Cotton, C.: Explanation and justification in machine learning: a survey. In: IJCAI 2017, Workshop on Explainable AI (2017)

    Google Scholar 

  16. Bizer, C., et al.: DBpedia - a crystallization point for the web of data. Web Semant. 7(3), 154–165 (2009)

    Article  Google Scholar 

  17. Blockeel, H.: Data mining: from procedural to declarative approaches. New Gener. Comput. 33(2), 115–135 (2015)

    Article  Google Scholar 

  18. Blockeel, H.: Declarative data analysis. Int. J. Data Sci. Anal., 1–7 (2017)

    Google Scholar 

  19. Carnap, R.: Logical Foundations of Probability (1962)

    Google Scholar 

  20. Chapman, P., et al.: CRISP-DM 1.0: Step-by-Step Data Mining Guide. CRISP-DM consortium: NCR Systems Engineering, DaimlerChrysler AG, SPSS Inc. and OHRA Verzekeringen en Bank Groep B.V (2000)

    Google Scholar 

  21. De Raedt, L., Kersting, K.: Statistical relational learning. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 916–924. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_786

    Chapter  Google Scholar 

  22. Dou, D., Wang, H., Liu, H.: Semantic data mining: a survey of ontology-based approaches. In: IEEE ICSC, pp. 244–251. IEEE (2015)

    Google Scholar 

  23. Duivesteijn, W., Knobbe, A., Feelders, A., van Leeuwen, M.: Subgroup discovery meets Bayesian networks-an exceptional model mining approach. In: Proceedings of International Conference on Data Mining (ICDM), pp. 158–167. IEEE, Washington, DC (2010)

    Google Scholar 

  24. Duivesteijn, W., Feelders, A., Knobbe, A.J.: Different slopes for different folks: mining for exceptional regression models with Cook’s distance. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 868–876. ACM, New York (2012)

    Google Scholar 

  25. Duivesteijn, W., Feelders, A.J., Knobbe, A.: Exceptional model mining. Data Min. Knowl. Disc. 30(1), 47–98 (2016)

    Article  MathSciNet  Google Scholar 

  26. Duivesteijn, W., Thaele, J.: Understanding where your classifier does (Not) work - the SCaPE model class for EMM. In: Proceedings of ICDM, pp. 809–814. IEEE (2014)

    Google Scholar 

  27. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press (1996)

    Google Scholar 

  28. Gamberger, D., Lavrac, N., Wettschereck, D.: Subgroup visualization: a method and application in population screening. In: Proceedings of IDAMAP (2002)

    Google Scholar 

  29. Gaskin, C.J., Happell, B.: On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use. Int. J. Nurs. Stud. 51(3), 511–521 (2014)

    Article  Google Scholar 

  30. Goethals, B., Moens, S., Vreeken, J.: MIME: a framework for interactive visual pattern mining. In: Proceedings of ACM SIGKDD, pp. 757–760. ACM (2011)

    Google Scholar 

  31. Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a “right to explanation”. arXiv preprint arXiv:1606.08813 (2016)

  32. Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., Giannotti, F.: A survey of methods for explaining black box models. arXiv preprint arXiv:1802.01933 (2018)

  33. Henelius, A., Puolamäki, K., Boström, H., Asker, L., Papapetrou, P.: A peek into the black box: exploring classifiers by randomization. Data Min. Knowl. Discov. 28(5–6), 1503–1529 (2014)

    Article  MathSciNet  Google Scholar 

  34. Henelius, A., et al.: GoldenEye++: a closer look into the black box. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS (LNAI), vol. 9047, pp. 96–105. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17091-6_5

    Chapter  Google Scholar 

  35. Henelius, A., Puolamäki, K., Ukkonen, A.: Interpreting classifiers through attribute interactions in datasets. In: Proceedings of 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017) (2017)

    Google Scholar 

  36. Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. 194, 28–61 (2013)

    Article  MathSciNet  Google Scholar 

  37. Jaroszewicz, S., Simovici, D.A.: Interestingness of frequent itemsets using Bayesian networks as background knowledge. In: Proceedings of SIGKDD, pp. 178–186. ACM (2004)

    Google Scholar 

  38. Kaytoue, M., Plantevit, M., Zimmermann, A., Bendimerad, A., Robardet, C.: Exceptional contextual subgraph mining. Mach. Learn. 106(8), 1171–1211 (2017)

    Article  MathSciNet  Google Scholar 

  39. Keim, D., Ward, M.: Visualization. In: Berthold, M., Hand, D.J. (eds.) Intelligent Data Analysis. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-48625-1_11

    Chapter  Google Scholar 

  40. Klösgen, W.: Explora: a multipattern and multistrategy discovery assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI Press (1996)

    Google Scholar 

  41. Klösgen, W.: Subgroup discovery. In: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002). Chap. 16.3

    Google Scholar 

  42. Klösgen, W., Lauer, S.R.W.: Visualization of data mining results. In: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002). Chap. 20.1

    Google Scholar 

  43. Knobbe, A.J., Cremilleux, B., Fürnkranz, J., Scholz, M.: From local patterns to global models: the LeGo approach to data mining. In: From Local Patterns to Global Models: Proceedings of the ECML/PKDD-08 Workshop (LeGo 2008), pp. 1–16 (2008)

    Google Scholar 

  44. Kolodner, J.L.: Making the implicit explicit: clarifying the principles of case-based reasoning. In: Case-based Reasoning: Experiences, Lessons and Future Directions, pp. 349–370 (1996)

    Google Scholar 

  45. Lavrač, N.: Subgroup discovery techniques and applications. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 2–14. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_2

    Chapter  Google Scholar 

  46. Lavrac, N., Kavsek, B., Flach, P., Todorovski, L.: Subgroup discovery with CN2-SD. J. Mach. Learn. Res. 5, 153–188 (2004)

    MathSciNet  Google Scholar 

  47. Leman, D., Feelders, A., Knobbe, A.: Exceptional model mining. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 1–16. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87481-2_1

    Chapter  Google Scholar 

  48. Lemmerich, F., Becker, M., Atzmueller, M.: Generic pattern trees for exhaustive exceptional model mining. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 277–292. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33486-3_18

    Chapter  Google Scholar 

  49. Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proceedings of International Conference on Data Mining (ICDM), pp. 369–376. IEEE Computer Society (2001)

    Google Scholar 

  50. Li, X., Huan, J.: Constructivism learning: a learning paradigm for transparent predictive analytics. In: Proceedings of SIGKDD, pp. 285–294. ACM (2017)

    Google Scholar 

  51. Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11. ACM (2003)

    Google Scholar 

  52. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007)

    Article  MathSciNet  Google Scholar 

  53. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of SIGKDD, pp. 80–86. AAAI Press, August 1998

    Google Scholar 

  54. Maher, P.: Explication defended. Studia Logica 86(2), 331–341 (2007)

    Article  MathSciNet  Google Scholar 

  55. Mandel, D.R.: Counterfactual and causal explanation: from early theoretical views to new frontiers. In: The Psychology of Counterfactual Thinking, pp. 23–39. Routledge (2007)

    Google Scholar 

  56. Mitzlaff, F., Atzmueller, M., Hotho, A., Stumme, G.: The social distributional hypothesis. J. Soc. Netw. Anal. Min. 4(216), 1–14 (2014)

    Google Scholar 

  57. Mitzlaff, F., Atzmueller, M., Stumme, G., Hotho, A.: Semantics of user interaction in social media. In: Ghoshal, G., Poncela-Casasnovas, J., Tolksdorf, R. (eds.) Complex Networks IV. SCI, vol. 476. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36844-8_2

    Chapter  Google Scholar 

  58. Morik, K.: Detecting interesting instances. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds.) Pattern Detection and Discovery. LNCS (LNAI), vol. 2447, pp. 13–23. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45728-3_2

    Chapter  Google Scholar 

  59. Morik, K., Boulicaut, J.-F., Siebes, A. (eds.): Local Pattern Detection. LNCS (LNAI), vol. 3539. Springer, Heidelberg (2005). https://doi.org/10.1007/b137601

    Book  Google Scholar 

  60. Morshed, A., Dutta, R., Aryal, J.: Recommending environmental knowledge as linked open data cloud using semantic machine learning. In: Proceedings of IEEE ICDEW, pp. 27–28. IEEE (2013)

    Google Scholar 

  61. Musto, C., Narducci, F., Lops, P., de Gemmis, M., Semeraro, G.: Linked open data-based explanations for transparent recommender systems. Int. J. Hum.-Comput. Stud. (2018)

    Google Scholar 

  62. Paulheim, H.: Explain-a-LOD: using linked open data for interpreting statistics. In: Proceedings of ACM IUI, pp. 313–314. ACM (2012)

    Google Scholar 

  63. Paulheim, H.: Generating possible interpretations for statistics from linked open data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 560–574. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30284-8_44

    Chapter  Google Scholar 

  64. Paulheim, H., Fümkranz, J.: Unsupervised generation of data mining features from linked open data. In: Proceedings of WIMS, p. 31. ACM (2012)

    Google Scholar 

  65. Pujara, J., Miao, H., Getoor, L., Cohen, W.: Large-scale knowledge graph identification using PSL. In: AAAI Fall Symposium on Semantics for Big Data (2013)

    Google Scholar 

  66. Rauch, J., Šimůnek, M.: Learning association rules from data through domain knowledge and automation. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 266–280. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09870-8_20

    Chapter  Google Scholar 

  67. Ribeiro, M.T., Singh, S., Guestrin, C.: Model-agnostic interpretability of machine learning. In: Proceedings of 2016 ICML Workshop on Human Interpretability in Machine Learning (2016)

    Google Scholar 

  68. Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of ACM SIGKDD, pp. 1135–1144. ACM (2016)

    Google Scholar 

  69. Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. AAAI (2018)

    Google Scholar 

  70. Richardson, M., Domingos, P.: Learning with knowledge from multiple experts. In: Proceedings of ICML, pp. 624–631. AAAI Press (2003)

    Google Scholar 

  71. Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)

    Article  Google Scholar 

  72. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. Web Semant. 36, 1–22 (2016)

    Article  Google Scholar 

  73. Roth-Berghofer, T., Schulz, S., Leake, D., Bahls, D.: Explanation-aware computing. AI Mag. 28(4) (2007)

    Google Scholar 

  74. Roth-Berghofer, T.R., Cassens, J.: Mapping goals and kinds of explanations to the knowledge containers of case-based reasoning systems. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 451–464. Springer, Heidelberg (2005). https://doi.org/10.1007/11536406_35

    Chapter  Google Scholar 

  75. Seipel, D., Nogatz, F., Abreu, S.: Domain-specific languages in prolog for declarative expert knowledge in rules and ontologies. Comput. Lang. Syst. Struct. 51, 102–117 (2018)

    Google Scholar 

  76. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of IEEE Symposium on Visual Languages, Boulder, Colorado, pp. 336–343 (1996)

    Google Scholar 

  77. Sørmo, F., Cassens, J., Aamodt, A.: Explanation in case-based reasoning - perspectives and goals. Artif. Intell. Rev. 24(2), 109–143 (2005)

    Article  Google Scholar 

  78. Spenke, M.: Visualization and interactive analysis of blood parameters with InfoZoom. Artif. Intell. Med. 22(2), 159–172 (2001)

    Article  Google Scholar 

  79. Spenke, M., Beilken, C.: Visual, interactive data mining with InfoZoom - the financial dataset. In: Workshop Notes on Discovery Challenge at the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 15–18 (1999)

    Google Scholar 

  80. Spieker, P.: Natürlichsprachliche Erklärungen in technischen Expertensystemen. Dissertation, University of Kaiserslautern (1991)

    Google Scholar 

  81. Thabtah, F.: A review of associative classification mining. Knowl. Eng. Rev. 22(1), 37–65 (2007)

    Article  Google Scholar 

  82. Theus, M.: Interactive data visualization using Mondrian. J. Stat. Softw. 7(11), 1–9 (2003)

    Google Scholar 

  83. Tiddi, I., d’Aquin, M., Motta, E.: An ontology design pattern to define explanations. In: Proceedings of K-Cap. ACM, New York (2015)

    Google Scholar 

  84. Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable predictions of tree-based ensembles via actionable feature tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 465–474. ACM (2017)

    Google Scholar 

  85. Van Deursen, A., Klint, P., Visser, J.: Domain-specific languages: an annotated bibliography. ACM Sigplan Not. 35(6), 26–36 (2000)

    Article  Google Scholar 

  86. Leeuwen, M.: Interactive data exploration using pattern mining. In: Holzinger, A., Jurisica, I. (eds.) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. LNCS, vol. 8401, pp. 169–182. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43968-5_9

    Chapter  Google Scholar 

  87. Vavpetič, A., Lavrač, N.: Semantic subgroup discovery systems and workflows in the SDM-Toolkit. Comput. J. 56(3), 304–320 (2013)

    Article  Google Scholar 

  88. Vavpetic, A., Podpecan, V., Lavrac, N.: Semantic subgroup explanations. J. Intell. Inf. Syst. 42(2), 233–254 (2014)

    Article  Google Scholar 

  89. Velicer, W.F., Eaton, C.A., Fava, J.L.: Construct explication through factor or component analysis: a review and evaluation of alternative procedures for determining the number of factors or components. In: Goffin, R.D., Helmes, E. (eds.) Problems and Solutions in Human Assessment: Honoring Douglas N. Jackson at Seventy, pp. 41–71. Springer, Boston (2000). https://doi.org/10.1007/978-1-4615-4397-8_3

    Chapter  Google Scholar 

  90. Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR (2017)

    Google Scholar 

  91. Wick, M.R., Thompson, W.B.: Reconstructive expert system explanation. Artif. Intell. 54(1–2), 33–70 (1992)

    Article  Google Scholar 

  92. Wilcke, X., Bloem, P., de Boer, V.: The knowledge graph as the default data model for learning on heterogeneous knowledge. Data Sci. (Preprint), 1–19 (2017)

    Google Scholar 

  93. Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of 4th International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 29–39. Morgan Kaufmann (2000)

    Google Scholar 

  94. Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63223-9_108

    Chapter  Google Scholar 

  95. Zelezny, F., Lavrac, N., Dzeroski, S.: Using constraints in relational subgroup discovery. In: Proceedings of International Conference on Methodology and Statistics, pp. 78–81. University of Ljubljana (2003)

    Google Scholar 

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  1. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, Netherlands

    Martin Atzmueller

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  1. Martin Atzmueller
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  1. Universität Würzburg, Wuerzburg, Germany

    Dietmar Seipel

  2. Christian-Albrechts-Universität zu Kiel, Kiel, Germany

    Michael Hanus

  3. Universidade de Èvora, Evora, Portugal

    Salvador Abreu

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Atzmueller, M. (2018). Declarative Aspects in Explicative Data Mining for Computational Sensemaking. In: Seipel, D., Hanus, M., Abreu, S. (eds) Declarative Programming and Knowledge Management. WFLP WLP INAP 2017 2017 2017. Lecture Notes in Computer Science(), vol 10997. Springer, Cham. https://doi.org/10.1007/978-3-030-00801-7_7

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