Chapter

Discovery Science

Volume 8777 of the series Lecture Notes in Computer Science pp 1-12

Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization

  • Prem Raj AdhikariAffiliated withHelsinki Institute for Information Technology HIIT and Department of Information and Computer Science, Aalto University School of Science
  • , Anže VavpetičAffiliated withJožef Stefan Institute and Jožef Stefan International Postgraduate School
  • , Jan KraljAffiliated withJožef Stefan Institute and Jožef Stefan International Postgraduate School
  • , Nada LavračAffiliated withJožef Stefan Institute and Jožef Stefan International Postgraduate School
  • , Jaakko HollménAffiliated withHelsinki Institute for Information Technology HIIT and Department of Information and Computer Science, Aalto University School of Science

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Abstract

Semi-automated data analysis is possible for the end user if data analysis processes are supported by easily accessible tools and methodologies for pattern/model construction, explanation, and exploration. The proposed three–part methodology for multiresolution 0–1 data analysis consists of data clustering with mixture models, extraction of rules from clusters, as well as data, cluster, and rule visualization using banded matrices. The results of the three-part process—clusters, rules from clusters, and banded structure of the data matrix—are finally merged in a unified visual banded matrix display. The incorporation of multiresolution data is enabled by the supporting ontology, describing the relationships between the different resolutions, which is used as background knowledge in the semantic pattern mining process of descriptive rule induction. The presented experimental use case highlights the usefulness of the proposed methodology for analyzing complex DNA copy number amplification data, studied in previous research, for which we provide new insights in terms of induced semantic patterns and cluster/pattern visualization.

Keywords

Mixture Models Semantic Pattern Mining Pattern Visualization