Summarising Data by Clustering Items
Abstract
For a book, the title and abstract provide a good first impression of what to expect from it. For a database, getting a first impression is not so straightforward. While low-order statistics only provide limited insight, mining the data quickly provides too much detail. In this paper we propose a middle ground, and introduce a parameter-free method for constructing high-quality summaries for binary data. Our method builds a summary by grouping items that strongly correlate, and uses the Minimum Description Length principle to identify the best grouping —without requiring a distance measure between items. Besides offering a practical overview of which attributes interact most strongly, these summaries are also easily-queried surrogates for the data. Experiments show that our method discovers high-quality results: correlated attributes are correctly grouped and the supports of frequent itemsets are closely approximated.
Cite
Text
Mampaey and Vreeken. "Summarising Data by Clustering Items." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15883-4_21Markdown
[Mampaey and Vreeken. "Summarising Data by Clustering Items." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/mampaey2010ecmlpkdd-summarising/) doi:10.1007/978-3-642-15883-4_21BibTeX
@inproceedings{mampaey2010ecmlpkdd-summarising,
title = {{Summarising Data by Clustering Items}},
author = {Mampaey, Michael and Vreeken, Jilles},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {321-336},
doi = {10.1007/978-3-642-15883-4_21},
url = {https://mlanthology.org/ecmlpkdd/2010/mampaey2010ecmlpkdd-summarising/}
}