Identifying Most Predictive Items

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KEIM, Daniel A., Markus WAWRYNIUK, 2004. Identifying Most Predictive Items. Proceedings of the Intl. Workshop on Pattern Representation and Management, Heraklion, Hellas, March 18, 2004. Heraklion, Hellas, 18. Mär 2004 - 18. Mär 2004. In: THEODORIDIS, Yannis, ed., Panos VASSILIADIS, ed.. PaRMa 2004 : Proceedings of the Intl. Workshop on Pattern Representation and Management, Heraklion, Hellas, March 18, 2004. Proceedings of the Intl. Workshop on Pattern Representation and Management, Heraklion, Hellas, March 18, 2004. Heraklion, Hellas, 18. Mär 2004 - 18. Mär 2004, 6. ISSN 1613-0073

@inproceedings{Keim2004Ident-41036, title={Identifying Most Predictive Items}, url={http://dblp.org/db/conf/parma/parma2004.html}, year={2004}, number={96}, issn={1613-0073}, series={CEUR Workshop Proceedings}, booktitle={PaRMa 2004 : Proceedings of the Intl. Workshop on Pattern Representation and Management, Heraklion, Hellas, March 18, 2004}, editor={Theodoridis, Yannis and Vassiliadis, Panos}, author={Keim, Daniel A. and Wawryniuk, Markus}, note={Article Number: 6} }

Identifying Most Predictive Items Frequent itemsets and association rules are generally accepted concepts in analyzing item-based databases. The Apriori-framework was developed for analyzing categorical data. However, many data include numerical values. Therefore, most existing techniques transform numerical values to categorical values. The transformation is done such that the rules are optimal with respect to support or confidence.<br />In this paper we choose a different approach for analyzing data with numerical and categorical data. We present methods to identify items, which have a strong impact on a given numerical attribute. With other words, we want to identify items, whose occurrence in an itemset allows us to make predictions about the distribution function of the numerical attribute. 2018-01-10T10:59:06Z 2004 Wawryniuk, Markus Keim, Daniel A. eng Wawryniuk, Markus Keim, Daniel A. 2018-01-10T10:59:06Z

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