# Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

arXiv: https://arxiv.org/abs/2303.13191 · HTML: https://mlsanity.com/paper/2303.13191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [Aspect-based Sentiment Analysis of Scientific Reviews](https://mlsanity.com/paper/2006.03257/) (2020)
- [Correlated Utility-based Pattern Mining](https://mlsanity.com/paper/1904.03336/) (2019)
- [Using Answer Set Programming for pattern mining](https://mlsanity.com/paper/1409.7777/) (2014)
- [Hybrid ASP-based Approach to Pattern Mining](https://mlsanity.com/paper/1808.07302/) (2018)
- [Constraints, Lazy Constraints, or Propagators in ASP Solving: An Empirical Analysis](https://mlsanity.com/paper/1707.04027/) (2017)
- [Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks](https://mlsanity.com/paper/1711.05090/) (2017)
- [The External Interface for Extending WASP](https://mlsanity.com/paper/1811.01692/) (2018)

## Abstract (arXiv)

Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High Utility Pattern Mining (HUPM); in particular we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach. Under consideration in Theory and Practice of Logic Programming (TPLP)