New Trends in Knowledge Driven Data Mining

Cláudia Antunes, Andreia Silva

2014

Abstract

Existing mining algorithms, from classification to pattern mining, reached considerable levels of efficiency, and their extension to deal with more demanding data, such as data streams and big data, show their incontestable quality and adequacy to the problem. Despite their efficiency, their effectiveness on identifying useful information is somehow impaired, not allowing for making use of existing domain knowledge to focus the discovery. The use of this knowledge can bring significant benefits to data mining applications, by resulting in simpler and more interesting and usable models. However, most of existing approaches are concerned with being able to mine specific domains, and therefore are not easily reusable, instead of building general algorithms that are able to incorporate domain knowledge, independently of the domain. In our opinion, this requires a drift in the focus of the research in data mining, and we argue this change should be from domain-driven to knowledge-driven data mining, aiming for a stronger emphasis on the exploration of existing domain knowledge for guiding existing algorithms.

References

  1. Agrawal, R and Srikant, R 1994, 'Fast Algorithms for Mining Association Rules', Int'l Conf on Very Large Data Bases, Morgan Kaufmann, Chile.
  2. Antunes, C 2009, 'Mining Patterns in the Presence of Domain Knowledge', Int'l Conf on Enterprise Information Systems, INSTICC, Italy.
  3. Antunes, C and Bebiano, T 2012, 'Mining Patterns with Domain Knowledge: a case study on multi-language data. In', Int'l Conf on Information Systems (ACIS ICIS 2012), IEEE Press.
  4. Baader, Franz, Calvanese, D, McGuinness, DL, Nardi, D, Patel-Schneider, PF (eds.) 2003, The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press.
  5. Cao, L 2008, 'Domain driven data mining (d3m)78, 2008 IEEE Int. Conf. on Data Mining Workshops (DDDM 08), IEEE Computer Society.
  6. Cao, L 2010, 'Domain-Driven Data Mining: Challenges and Prospects', IEEE Transactions on Knowledge and Data Engineering, vol 22, no. 6, pp. 755-769.
  7. Cao, L, Luo, D and Zhang, C 2007, 'Knowledge actionability: satisfying technical and business interestingness', Int'l Journal of Business Intelligence and Data Mining, vol 2, no. 4, p. 496-514.
  8. Cao, L, Yu, P, Zhang, C and Zhang, H 2010, Data Mining for Business Applications, Springer.
  9. Cao, L and Zhang, C 2006, 'Domain-driven data mining: A practical methodology', Int. Journal Data Warehousing and Mining, vol 2, no. 4, p. 49-65.
  10. Chandrasekaran, B, Josephson, JR and Benjamins, VR 1999, 'What Are Ontologies, and Why Do We Need Them?78, IEEE Intelligent Systems, vol 14, no. 1, p. 20- 26.
  11. Diamantini, C and Potena, D 2008, 'Semantic annotation and services for kdd tools sharing and reuse 78, 2008 IEEE Int. Conf. on Data Mining Workshops (ICDMW 08), IEEE, Pisa, Italy.
  12. Druck, G and McCallum, A 2011, 'Toward interactive training and evaluation', ACM Int'l Conf on Information and Knowledge Management, ACM.
  13. Dzeroski, S 1996, 'Inductive logic programming and knowledge discovery in databases', in Advances in Knowledge Discovery and Data Mining, MIT Press.
  14. Goethals, B and Bussche, J 2000, 'On Supporting Interactive Association Rule Mining', Int'l Conf Data Warehousing and Knowledge Discovery, Springer.
  15. Goethals, B, Moens, S and Vreeken, J 2011, 'MIME: a framework for interactive visual pattern mining', ACM SIGKDD Int'l Conf on Knowledge discovery and data mining (KDD 11), ACM.
  16. Han, J, Pei, J and Yin, Y 2000, 'Mining Frequent Patterns without Candidate Generation', Int'l Conf. on Management of Data, ACM Press, TX.
  17. Heckerman, D, Geiger, D and Chickering, DM 1995, 'Learning Bayesian Networks: The Combination of Knowledge and Statistical Data', Machine Learning, vol 20, pp. 197-243.
  18. Jozefowska, J, Lawrynowicz, A and Lukaszewski, T 2010, 'The role of semantics in mining frequent patterns from knowledge bases in description logics with rules', Theory Practical Logical Programming, vol 10, no. 3, p. 251-289.
  19. Kononenko, I 1997, 'Machine learning for medical diagnosis: history, state of the art and perspective', in RS Michalski, I Bratko, M Kubat (eds.), Machine Learning and Data Mining: Methods and Applications, Wiley.
  20. Lavrac, N, Vavpetic, A, Soldatova, LN, Trajkovski, I and Novak, PK 2011, 'Using ontologies in semantic data mining with segs and g-segs', Int'l Conf on Discovery Science (DS 11), Finland.
  21. Levy, A and Rousset, M-C 1998, 'Combining horn rules and description logics in carin', Artificial Intelligence, vol 104, no. 1, p. 165-209.
  22. Lisi, F and Esposito, F 2009, 'On ontologies as prior conceptual knowledge in inductive logic programming', Studies in Computational Intelligence, vol 220, p. 3-17.
  23. Lisi, F and Malerba, D 2004, 'Inducing multi-level association rules from multiple relations', Machine Learning, vol 55, no. 2, p. 175-210.
  24. Liu, H 2010, 'Towards semantic data mining', Int'l Semantic Web Conf. (ISWC 10).
  25. Malerba, D and Lisi, F 2001, 'Discovering associations between spatial objects: An ilp application', Int'l Conf. on Inductive Logic Programming (ILP 01), SpringerVerlag, London, UK.
  26. Nag, B, Deshpande, PM and DeWitt, DJ 1999, 'Using a knowledge cache for interactive discovery of association rules', ACM SIGKDD Int'l Conf Knowledge Discovery and Data Mining, ACM.
  27. Nienhuys-Cheng, S-H and Wolf, RD 1997, Foundations of Inductive Logic Programming, Springer-Verlag.
  28. Novak, P, Vavpetic, A, Trajkovski, I and Lavra?c, N 2009, 'Towards semantic data mining with g-segs', Int'l Multiconference Information Society (IS 09).
  29. Pearl, J 1988, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann.
  30. Raedt, LD and Ramon, J 2004, 'Condensed representations for inductive logic programming', Int'l Conf. on Principles of Knowledge Representation and Reasoning, AAAI Press.
  31. Rouveirol, C and Ventos, V 2000, 'Towards learning in carin-aln', Int'l Conf. on Inductive Logic Programming (ILP 00), Springer-Verlag.
  32. Silva, A and Antunes, C 2013, 'Pushing Constraints into a Pattern-Tree', Int'l Conf. on Modeling Decisions for Artificial Intelligence (MDAI 2013), Springer.
  33. Srikant, R and Agrawal, R 1995, 'Mining Generalized Association Rules', Int'l Conf on Very Large Databases, Morgan Kaufmann, Switzerland.
  34. Wirth, R and Hipp, J 2000, 'CRISP-DM: Towards a Standard Process Model for Data Mining', Int'l Conf. on the Practical Application of Knowledge Discovery and Data Mining.
  35. Zhang, J, Silvescu, A and Honavar, V 2002, 'Ontologydriven induction of decision trees at multiple levels of abstraction', in Abstraction, reformulation, and approximation, Springer.
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Paper Citation


in Harvard Style

Antunes C. and Silva A. (2014). New Trends in Knowledge Driven Data Mining . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 346-351. DOI: 10.5220/0004974003460351


in Bibtex Style

@conference{iceis14,
author={Cláudia Antunes and Andreia Silva},
title={New Trends in Knowledge Driven Data Mining},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2014},
pages={346-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004974003460351},
isbn={978-989-758-027-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - New Trends in Knowledge Driven Data Mining
SN - 978-989-758-027-7
AU - Antunes C.
AU - Silva A.
PY - 2014
SP - 346
EP - 351
DO - 10.5220/0004974003460351