Abstract
Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operations on data using a special-purpose language, powerful enough to perform all the required manipulations, such as data preprocessing, pattern discovery and pattern post-processing. In this paper we present a comparison between query languages (MSQL, DMQL and MINE RULE) that have been proposed for association rules extraction in the last years and discuss their common features and differences. We present them using a set of examples, taken from the real practice of data mining. This allows us to define the language design guidelines, with particular attention to the open issues on IDBs.
Project (IST 2000-26469) partially funded by the EC IST Programme - FET.
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References
Boulicaut J-F., Jeudy B.: Mining free-sets under constraints. Proc. of Database Engineering & Applications Symposium, IDEAS’01, Grenoble, France (2001).
Imielinski, T., Mannila, H.: A Database Perspective on Knowledge Discovery. Communications of the ACM. 3:4 (1996) 58–64.
Imielinski, T., Virmani, A., Abdulghani, A.: DataMine: Application Programming Interface and Query Language for Database Mining. Proc. of the 2nd Int. Conf. on Knowledge Discovery and Data Mining, KDD’96. 3 (1996) 256–261.
Imielinski, T., Virmani, A.: MSQL: A Query Language for Database Mining. Data Mining and Knowledge Discovery. 3 (1999) 373–408.
Virmani, A.: Second Generation Data Mining. PhD Thesis, Rutgers Univ. (1998).
Han, J., Fu, Y., Wang, W., Koperski, K., Zaiane, O.: DMQL: A Data Mining Query Language for Relational Databases.
Han, J., Kamber, M.: Data Mining — Concepts and Techniques. Morgan Kaufmann Publishers (2001).
Meo, R., Psaila, G., Ceri, S.: A New SQL-like Operator for Mining Association Rules. Proc. of the 22nd Int. Conf. of Very Large Data Bases. Bombay, India (1996).
Meo, R., Psaila, G., Ceri, S.: An Extension to SQL for Mining Association Rules. Data Mining and Knowledge Discovery. 9:4 (1997).
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© 2002 Springer-Verlag Berlin Heidelberg
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Botta, M., Boulicaut, JF., Masson, C., Meo, R. (2002). A Comparison between Query Languages for the Extraction of Association Rules. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2002. Lecture Notes in Computer Science, vol 2454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46145-0_1
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DOI: https://doi.org/10.1007/3-540-46145-0_1
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