# Large-Scale Mode Identification and Data-Driven Sciences

arXiv: https://arxiv.org/abs/1509.06428 · HTML: https://mlsanity.com/paper/1509.06428/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Abductive Inference and C. S. Peirce: 150 Years Later](https://mlsanity.com/paper/2111.08054/) (2021)

## Abstract (arXiv)

Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are available for automatic (not requiring manual case-by-base investigation), objective (not subjective), and nonparametric (not based on restrictive parametric model assumptions) mode discovery, which can scale to large data sets. This article introduces LPMode--an algorithm based on a new theory for detecting multimodality of a probability density. We apply LPMode to answer important research questions arising in various fields from environmental science, ecology, econometrics, analytical chemistry to astronomy and cancer genomics.