ML Anthology

Data Mining Meets HCI: Data and Visual Analytics of Frequent Patterns

Carson K. Leung, Christopher L. Carmichael, Yaroslav Hayduk, Fan Jiang, Vadim V. Kononov, Adam G. M. Pazdor
ECML-PKDD 2016 pp. 289-293
doi:10.1007/978-3-319-46131-1_37 /ecmlpkdd/2016/leung2016ecmlpkdd-data/

Abstract

As a popular data mining tasks, frequent pattern mining discovers implicit, previously unknown and potentially useful knowledge in the form of sets of frequently co-occurring items or events. Many existing data mining algorithms return to users with long textual lists of frequent patterns, which may not be easily comprehensible. As a picture is worth a thousand words, having a visual means for humans to interact with computers would be beneficial. This is when human-computer interaction (HCI) research meets data mining research. In particular, the popular HCI task of data and result visualization could help data miners to visualize the original data and to analyze the mined results (in the form of frequent patterns). In this paper, we present a few systems for data and visual analytics of frequent patterns, which integrate (i) data analytics and mining with (ii) data and result visualization.

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Cite

Text

Leung et al. "Data Mining Meets HCI: Data and Visual Analytics of Frequent Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_37

Markdown

[Leung et al. "Data Mining Meets HCI: Data and Visual Analytics of Frequent Patterns." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/leung2016ecmlpkdd-data/) doi:10.1007/978-3-319-46131-1_37

BibTeX

@inproceedings{leung2016ecmlpkdd-data,
  title     = {{Data Mining Meets HCI: Data and Visual Analytics of Frequent Patterns}},
  author    = {Leung, Carson K. and Carmichael, Christopher L. and Hayduk, Yaroslav and Jiang, Fan and Kononov, Vadim V. and Pazdor, Adam G. M.},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2016},
  pages     = {289-293},
  doi       = {10.1007/978-3-319-46131-1_37},
  url       = {https://mlanthology.org/ecmlpkdd/2016/leung2016ecmlpkdd-data/}
}