Mining outlying aspects on numeric data

Duan, Lei; Tang, Guanting; Pei, Jian; Bailey, James E.; Campbell, Akiko; Tang, Changjie

doi:10.1007/s10618-014-0398-2

Cited by 62 publications

(65 citation statements)

References 20 publications

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“…The latest work on outlying aspects mining can be categorized into two main directions, which we refer to as feature selection approaches [4], and score-andsearch approaches [5].…”

Section: Related Workmentioning

confidence: 99%

“…In [5], the kernel density estimate was proposed as an outlyingness measure. It is well known, however, that the raw density measure tends to be smaller for subspaces of higher dimensionality, as a result of increasing sparseness.…”

Section: Related Workmentioning

confidence: 99%

“…We compare our approaches with the density rank based approach in [5] and Local Outlier with Graph Projection (LOGP) [2]. LOGP is the closest method in spirit to OARank, in that it also learns a set of weights: features with higher weights contribute more to the outlyingness of the query.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…We implemented all methods in Matlab/C++ except LOGP for which the Matlab code was kindly provided by the authors. The parameters for all methods were set as recommended in the original articles [5,2]. The bandwidth parameter for OARank was set according to [5], i.e., h = 1.06 min{σ, with σ being the standard sample deviation, and R being the difference between the first and third quartiles of the data distribution.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In [5], this problem was coined outlying aspect mining, although it has also been known as outlying subspaces detection [15], outlier explanation [10], outlier interpretation [2], and object explanation [12]. Outlying aspects mining has many practical applications.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Scalable Outlying-Inlying Aspects Discovery via Feature Ranking

Vinh

Chan

Bailey

et al. 2015

Advances in Knowledge Discovery and Data Mining

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Abstract. In outlying aspects mining, given a query object, we aim to answer the question as to what features make the query most outlying. The most recent works tackle this problem using two different strategies. (i) Feature selection approaches select the features that best distinguish the two classes: the query point vs. the rest of the data. (ii) Score-and-search approaches define an outlyingness score, then search for subspaces in which the query point exhibits the best score. In this paper, we first present an insightful theoretical result connecting the two types of approaches. Second, we present OARank -a hybrid framework that leverages the efficiency of feature selection based approaches and the effectiveness and versatility of score-and-search based methods. Our proposed approach is orders of magnitudes faster than previously proposed score-and-search based approaches while being slightly more effective, making it suitable for mining large data sets.

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“…The latest work on outlying aspects mining can be categorized into two main directions, which we refer to as feature selection approaches [4], and score-andsearch approaches [5].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Scalable Outlying-Inlying Aspects Discovery via Feature Ranking

Vinh

Chan

Bailey

et al. 2015

Advances in Knowledge Discovery and Data Mining

Self Cite

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There and back again: Outlier detection between statistical reasoning and data mining algorithms

Zimek

Filzmoser

2018

WIREs Data Min & Knowl

136

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Outlier detection has been a topic in statistics for centuries. Over mainly the last two decades, there has been also an increasing interest in the database and data mining community to develop scalable methods for outlier detection. Initially based on statistical reasoning, however, these methods soon lost the direct probabilistic interpretability of the derived outlier scores. Here, we detail from a joint point of view of data mining and statistics the roots and the path of development of statistical outlier detection and of database‐related data mining methods for outlier detection. We discuss their inherent meaning, review approaches to again find a statistically meaningful interpretation of outlier scores, and sketch related current research topics. This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Scalable Statistical Methods Technologies > Machine Learning

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