Original scientific paper

https://doi.org/10.17559/TV-20171109130510

Improved Visualization of Frequent Itemset Relationships Using the Minimal Spanning Tree Algorithm

Mihaela Vranić ; FER, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Damir Pintar orcid.org/0000-0001-9589-7890 ; FER, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Frano Škopljanac-Mačina ; FER, University of Zagreb, Unska 3, 10000 Zagreb, Croatia

Abstract

Descriptive data mining techniques offer a way of extracting useful information out of large datasets and presenting it in an interpretable fashion to be used as a basis for future decisions. Since users interpret information most easily through visual means, techniques which produce concise, visually attractive results are usually preferred. We define a method, which converts transactional data into tree-like data structures, which depict important relationships between items contained in this data. The new approach we propose is offering a way to mitigate the loss of information present in previously developed algorithms, which use mined frequent itemsets and construct tree structures. We transfer the problem to the domain of graph theory and through minimal spanning tree construction achieve more informative visualizations. We highlight the new approach with comparison to previous ones by applying it on a real-life datasets – one connected to market basket data and the other from the educational domain.

Keywords

association rules; data mining; dendrograms; frequent itemsets; minimal spanning tree; transactional data; visual representation

Hrčak ID:

219508

URI

https://hrcak.srce.hr/219508

Publication date:

24.4.2019.

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