# Contrast Pattern Mining: A Survey

arXiv: https://arxiv.org/abs/2209.13556 · HTML: https://mlsanity.com/paper/2209.13556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 127 references that are in the map.
- First posted on arXiv 27 Sep 2022.

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

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning](https://mlsanity.com/paper/2009.10273/) (2020)
- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [A Survey of Parallel Sequential Pattern Mining](https://mlsanity.com/paper/1805.10515/) (2018)
- [Mining Density Contrast Subgraphs](https://mlsanity.com/paper/1802.06775/) (2018)
- [Multivariate Data Explanation by Jumping Emerging Patterns Visualization](https://mlsanity.com/paper/2106.11112/) (2021)
- [OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns](https://mlsanity.com/paper/1811.09842/) (2018)
- [Improving Scalability of Contrast Pattern Mining for Network Traffic Using Closed Patterns](https://mlsanity.com/paper/2011.14830/) (2020)

## Built on by (most cited first, 2 of 2)

- [Pattern-Based Graph Classification: Comparison of Quality Measures and Importance of Preprocessing](https://mlsanity.com/paper/2507.00039/) (2025)
- [Exact Subgraph Isomorphism Network with Mixed L0,2 Norm Constraint for Predictive Graph Mining](https://mlsanity.com/paper/2509.21699/) (2025)

## Abstract (arXiv)

Contrast pattern mining (CPM) is an important and popular subfield of data mining. Traditional sequential patterns cannot describe the contrast information between different classes of data, while contrast patterns involving the concept of contrast can describe the significant differences between datasets under different contrast conditions. Based on the number of papers published in this field, we find that researchers' interest in CPM is still active. Since CPM has many research questions and research methods. It is difficult for new researchers in the field to understand the general situation of the field in a short period of time. Therefore, the purpose of this article is to provide an up-to-date comprehensive and structured overview of the research direction of contrast pattern mining. First, we present an in-depth understanding of CPM, including basic concepts, types, mining strategies, and metrics for assessing discriminative ability. Then we classify CPM methods according to their characteristics into boundary-based algorithms, tree-based algorithms, evolutionary fuzzy system-based algorithms, decision tree-based algorithms, and other algorithms. In addition, we list the classical algorithms of these methods and discuss their advantages and disadvantages. Advanced topics in CPM are presented. Finally, we conclude our survey with a discussion of the challenges and opportunities in this field.