Abstract
The goal of the EverMiner project is to run automatic data mining process starting with several items of initial domain knowledge and leading to new knowledge being inferred. A formal description of items of domain knowledge as well as of all particular steps of the process is used. The EverMiner project is based on the LISp-Miner software system which involves several data mining tools. There are experiments with the proposed approach realized by manual chaining of tools of the LISp-Miner. The paper describes experiences with the LISp-Miner Control Language which allows to transform a formal description of data mining process into an executable program.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hájek, P., Havránek, T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, Heidelberg (1978)
Hájek, P., Havránek, T.: GUHA 80: An Application of Artificial Intelligence to Data Analysis. Computers and Artificial Intelligence 1, 107–134 (1982)
Hájek, P., Holeňa, M., Rauch, J.: The GUHA method and its meaning for data mining. J. Comput. Syst. Sci. 76, 34–48 (2010)
Ierusalimschy, R., Figueiredo, L.H., de Celes, W.: Lua an extensible extension language. Software: Practice & Experience 26, 635–652 (1996)
Mansingh, G., Osei-Bryson, K.-M., Reichgelt, H.: Using ontologies to facilitate post-processing of association rules by domain experts. Information Sciences 181, 419–434 (2011)
Phillips, J., Buchanan, B.G.: Ontology guided knowledge discovery in databases. In: Proc. First International Conference on Knowledge Capture, pp. 123–130. ACM, Victoria (2001)
Rauch, J.: Formalizing Data Mining with Association Rules. In: Proceedings of 2012 IEEE International Conference on Granular Computing (GRC 2012), pp. 406–411. IEEE Computer Society, Los Alamitos (2012)
Rauch, J.: EverMiner: consideration on knowledge driven permanent data mining process. International Journal of Data Mining, Modelling and Management 4(3), 224–243 (2012)
Rauch, J. (ed.): Observational Calculi and Association Rules. SCI, vol. 469. Springer, Berlin (2013)
Rauch, J., Šimůnek, M.: An Alternative Approach to Mining Association Rules. In: Lin, T.Y., Liau, C.-J., Ohsuga, S., Hu, X., Tsumoto, S. (eds.) Foundations of Data Mining and knowledge Discovery. SCI, vol. 6, pp. 211–231. Springer, Heidelberg (2005)
Rauch, J., Šimůnek, M.: Applying Domain Knowledge in AssociationRules Mining Process - First Experience. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS (LNAI), vol. 6804, pp. 113–122. Springer, Heidelberg (2011)
Šimůnek, M.: Academic KDD Project LISp-Miner. In: Abraham, A., Franke, K., Köppen, M. (eds.) Intelligent Systems Design and Applications. ASC, vol. 23, pp. 263–272. Springer, Tulsa (2003)
Šimůnek, M.: LISp-Miner Control Language – description of scripting language implementation. Submitted for publication in Journal of System Integration, http://www.si-journal.org ISSN: 1804-2724
Šimůnek, M., Rauch, J.: EverMiner – Towards Fully Automated KDD Process. In: Funatsu, K., Hasegava, K. (eds.) New Fundamental Technologies in Data Mining, pp. 221–240. InTech, Rijeka (2011)
Sharma, S., Osei-Bryson, K.-M.: Toward an integrated knowledge discovery and data mining process model. The Knowledge Engineering Review 25 49–67 (2010)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right objective measure for association analysis. Information Systems 29, 293–313 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Šimůnek, M., Rauch, J. (2014). EverMiner Prototype Using LISp-Miner Control Language. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-08326-1_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08325-4
Online ISBN: 978-3-319-08326-1
eBook Packages: Computer ScienceComputer Science (R0)