By Petra Perner
This booklet constitutes the refereed lawsuits 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 conscientiously reviewed and chosen from a variety of submissions. the subjects variety from theoretical elements of knowledge mining to functions of knowledge mining, equivalent to in multimedia info, in advertising, in drugs and agriculture, and in approach regulate, and society.
Read or Download Advances in Data Mining: Applications and Theoretical Aspects: 15th Industrial Conference, ICDM 2015, Hamburg, Germany, July 11-24, 2015, Proceedings (Lecture Notes in Computer Science) PDF
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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 (Lecture Notes in Computer Science)
E. not to look at it before they were asked to. g. it was mentioned that centroids contain modes and means, that decision trees have important attributes at the top and that rules may describe only a subset of a cluster. Fig. 3. Distribution of the age attribute in cluster 5 In the following, we report the questions of our questionnaire, along with our expectations regarding how each group of participants will react: Task 1. ” Fig. 15 in the centroid (see Fig. 1), but we expect the centroid group to fall into that trap according to H1.
Because of the small size of the data set 3-fold cross validation was applied instead of the usual 10-fold cross validation.
A rule representation will use attributes in the label that appear most frequently within the rules Another assumption is that, when we explicitly ask a human analyst for the importance of an attribute that is at a low level of a decision tree representation, there is a risk that (s)he forgets about the instructions (see Sect. 2) and conﬁrms that importance: H5: Given a decision tree cluster representation and the question of whether an attribute at a low level of the tree is important for the essence of the cluster, a human analyst will be misled to say that the attribute is important.