By Petra Perner

This e-book constitutes the refereed court cases of the fifteenth business convention on Advances in facts Mining, ICDM 2015, held in Hamburg, Germany, in July 2015.

The sixteen revised complete papers provided have been rigorously reviewed and chosen from quite a few submissions. the themes diversity from theoretical points of knowledge mining to purposes of information mining, reminiscent of in multimedia info, in advertising and marketing, in drugs and agriculture, and in strategy keep watch over, and society.

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Extra info for Advances in Data Mining: Applications and Theoretical Aspects: 15th Industrial Conference, ICDM 2015, Hamburg, Germany, July 11-24, 2015, Proceedings

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E. mainly “buy male magazines” and “home owner”. It is a bit surprising that “buy books” is not used although at second-highest position in the tree and that many participants in this group use age which can only be speculative since the tree does not contain the age attribute. We may assume that having carried out Task 1 before had an influence here – especially since the same people who speculated in Task 1 did the same here and their answers were consistent between Tasks 1 and 4. When inspecting the answers from the centroid group, we see that participants used exclusively demographic attributes to describe the cluster – which may be explained by the fact that they are at the top of the centroid representation (see Fig.

Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967) 10. html 11. 5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1993) 12. : A mixed-integer programming approach to the clustering problem with an application in customer segmentation. Eur. J. Oper. Res. 173(3), 866–879 (2006) 13. : Customer segmentation revisited: The case of the airline industry.

Past researches proved that some characteristics such as socioeconomic characteristics of the trade area, competition, sales area and special services that stores offer affect the performance of stores, which may lead us to find similar and dissimilar stores in terms of those characteristics [5, 7–9]. After segmenting stores using clustering, frequent itemsets and association rules analysis will be applied for each cluster for the purpose of target marketing. This way, we will be able to find the differences between the clusters in terms of the items sold at the stores.

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