# A new effective and efficient measure for outlying aspect mining

arXiv: https://arxiv.org/abs/2004.13550 · HTML: https://mlsanity.com/paper/2004.13550/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [Text Anomaly Detection with Simplified Isolation Kernel](https://mlsanity.com/paper/2510.13197/) (2025)
- [Outlier Explanation via Sum-Product Networks](https://mlsanity.com/paper/2207.08414/) (2022)
- [PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation](https://mlsanity.com/paper/2312.10968/) (2023)
- [A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data](https://mlsanity.com/paper/2601.01358/) (2026)

## Abstract (arXiv)

Outlying Aspect Mining (OAM) aims to find the subspaces (a.k.a. aspects) in which a given query is an outlier with respect to a given dataset. Existing OAM algorithms use traditional distance/density-based outlier scores to rank subspaces. Because these distance/density-based scores depend on the dimensionality of subspaces, they cannot be compared directly between subspaces of different dimensionality. Z-score normalisation has been used to make them comparable. It requires to compute outlier scores of all instances in each subspace. This adds significant computational overhead on top of already expensive density estimation---making OAM algorithms infeasible to run in large and/or high-dimensional datasets. We also discover that Z-score normalisation is inappropriate for OAM in some cases. In this paper, we introduce a new score called SiNNE, which is independent of the dimensionality of subspaces. This enables the scores in subspaces with different dimensionalities to be compared directly without any additional normalisation. Our experimental results revealed that SiNNE produces better or at least the same results as existing scores; and it significantly improves the runtime of an existing OAM algorithm based on beam search.