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Cited by 9 publications
(18 citation statements)
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References 11 publications
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“…This paper shows that the developed wordification technique is simple, considerably more efficient and at least as accurate as the comparable state-of-the-art propositionalization methods. This paper extends our previous research (Perovšek, Vavpetič, & Lavrač, 2012 in many ways. The related work is more extensively covered.…”
Section: Introductionmentioning
confidence: 77%
See 1 more Smart Citation
“…This paper shows that the developed wordification technique is simple, considerably more efficient and at least as accurate as the comparable state-of-the-art propositionalization methods. This paper extends our previous research (Perovšek, Vavpetič, & Lavrač, 2012 in many ways. The related work is more extensively covered.…”
Section: Introductionmentioning
confidence: 77%
“…After describing the relational databases used in this study, we describe the experiments performed on these datasets and provide a comparison of wordification to other propositionalization techniques. In comparison with the experimental setting described in Perovšek, Vavpetič, Cestnik, and Lavrač (2013), a larger number of datasets is used, and very favorable results are obtained by using decision tree learner J48, compared to relatively poor results reported in previous work, where the Naive Bayesian classifier assuming feature independence was used. In addition to the J48 tree learner, we also tested the LibSVM learner.…”
Section: Methodsmentioning
confidence: 92%
“…and wordification [47], an approach for unfolding relational databases into bagof-words representations. The approach, described in the following sections, relies on some of the key ideas initially introduced in the mentioned works on propositionalization, as taxonomies are inherently relational data structures.…”
Section: Learning From Graphs and Relational Informationmentioning
confidence: 99%
“…Semantic data mining approaches have been successfully applied to association rule learning [6], semantic subgroup discovery [7,8], data visualization [9], as well as to text classification [10]. Provision of semantic information allows the learner to use features on a higher semantic level, allowing for better data generalizations.…”
Section: Introductionmentioning
confidence: 99%
“…al [8] use propositionalization to apply clustering algorithms, like KMeans, to multi-relational data. Propositionalization for classification has been extensively explored [48,68,52,50]. ODDBALL extracts patterns from large weighted graphs and then uses those patterns as features to discover anomalous nodes in graph [5].…”
Section: Object-relational Datamentioning
confidence: 99%