Advances in Data Mining: Applications and Theoretical by Petra Perner

By Petra Perner

This booklet constitutes the refereed complaints of the fifteenth business convention on Advances in facts Mining, ICDM 2015, held in Hamburg, Germany, in July 2015.

The sixteen revised complete papers offered have been rigorously reviewed and chosen from a number of submissions. the themes diversity from theoretical elements of knowledge mining to functions of knowledge mining, corresponding to in multimedia info, in advertising, in drugs and agriculture, and in strategy keep an eye on, and society.

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

Sample text

We represented the two decision trees in a user-friendly graphical form. Thus, we had, for each of the two selected clusters, a centroid representation in a tabular format, a graphically represented tree and a set of text-based rules. Figure 1 shows the centroid representation of the two clusters – along with the mean and mode values for the full data set (which can be used for comparison and to detect potentially meaningful deviations). Figure 2 shows part of the tree representation and the full rule representation of cluster 5.

Syst. 41, 82–97 (2001) 3. : Network planning and retail store segmentation, a spatial clustering approach. Int. J. Appl. Geospatial Res. 4(1), 67–79 (2013) 4. : Product differentiation and market segmentation as alternative marketing strategies. J. Mark. 21, 3–8 (1956) 5. : Market segmentation and competitive analysis for supermarket retailing. Int. J. Retail Distrib. Manage. 22, 38–48 (1994) 6. : Building Data Mining Applications for CRM. McGraw-Hill, New York (1999) 7. : Retail analytics in the context of segmentation, targeting, optimisation of the operations of convenience store franchises.

From the answers, we collected evidence to answer our research questions Q1 and Q2. e. not using multiple choice, their answers had to be coded before a quantitative analysis became possible. Below, we report such analysis and discuss the results, at the same time explaining the codes that we formed inductively while reading the answers. Sometimes, coding required interpretation, especially when analysing the cluster labels that participants were asked to write down in Task 4. For instance, the tree representation of cluster 5 does not contain the gender attribute.

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