Answers are grounded in this paper and cite their sources.
Algorithm evaluation reveals efficient automated pattern discovery across multi-dimensional datasets, suggesting broad utility for general data analysis systems.
Discovering interesting data patterns is a common and important analytical need in data, with increasing user demand for automated discovery abilities. However, automatically discovering interesting patterns from multi-dimensional data remains challenging. Existing techniques focus on mining individual types of patterns. There is a lack of unified formulation for different pattern types, as well as general mining frameworks to derive them effectively and efficiently. We present a novel technique QuickInsights, which quickly and automatically discovers interesting patterns from multi-dimensional data. QuickInsights proposes a unified formulation of interesting patterns, called insights, and designs a systematic mining framework to discover high-quality insights efficiently. We demonstrate the effectiveness and efficiency of QuickInsights through our evaluation on 447 real datasets as well as user studies on both expert users and non-expert users. QuickInsights is released in Microsoft Power BI.
No takes yet. Share an insight, caveat, or question.
Ding et al. (2019) studied this question.
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: