Boundary-aware local Density-based outlier detection

Aydın, Fatih

doi:10.1016/j.ins.2023.119520

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…To quantify the degree of outliers, we define the feedback outlier factor as a positively correlated representation of outlierliness. Definition 5: Feedback Outlier Factor, FOF: The FOF for object x i is defined as the reciprocal of the density feedback value dfv(x i ), as shown in Equation (10).…”

Section: Feedback Outlier Factormentioning

confidence: 99%

“…From a technical standpoint, outlier detection algorithms mainly include statistics-based, clustering-based, and density-based methods. Statistics-based methods [7][8][9][10] typically assume a specific distribution model for the data, and treat points in low-probability areas as outliers, such as the Gaussian mixture model [7] and ECOD [8]. Clustering-based methods typically employ clustering techniques to partition dense points into clusters, identifying isolated points as outliers [11], such as DBSCAN [12], k-means [13] and FDPC [14] proposed in recent years.…”

Section: Introductionmentioning

confidence: 99%

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An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Zhang,

Hou,

Jia

et al. 2024

Preprint

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Neighborhood similarity assumes that adjacent objects should exhibit similar characteristics. To address the issue that previous density-based algorithms neglected neighborhood similarity and global information, resulting in low accuracy for outlier detection, we propose an outlier detection algorithm based on local density feedback outlier factor. Firstly, principal component analysis method is employed combining with natural neighbor search algorithm for density estimation. Subsequently, a density feedback mechanism is introduced for post-processing density values, which defines the density differences within the neighborhood as feedback signals, and update density feedback values for each object based on the principle of neighborhood similarity. Through multiple iterations of feedback, individual objects can aggregate global information. Finally, feedback outlier factor is defined to quantify outlier degrees. Experimental validation demonstrates the effectiveness of the proposed algorithm in enhancing outlier detection performance.

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Section: Feedback Outlier Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Outlier Detection Algorithm Based on Local Density Feedback Outlier Factor

Zhang,

Hou,

Jia

et al. 2024

Preprint

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“…Experimental results demonstrated that this algorithm not only reduced computational complexity and execution time but also improved the accuracy of outlier detection. Aydın (2023) employed inequalities to delineate neighborhood boundaries for data points and detected outliers by quantifying the density of their neighborhoods, yielding effective results. Table 2 summarizes these outlier detection algorithms and their characteristics.…”

Section: Bldodmentioning

confidence: 99%

Pre-Cutoff Value Calculation Method for Accelerating Metric Space Outlier Detection

Xu,

Liang,

Huang

et al. 2023

International Journal of Grid and High Performance Computing

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Outlier detection is an important data mining technique. In this article, the triangle inequality of distances is leveraged to design a pre-cutoff value (PCV) algorithm that calculates the outlier degree pre-threshold without additional distance computations. This algorithm is suitable for accelerating various metric space outlier detection algorithms. Experimental results on multiple real datasets demonstrate that the PCV algorithm reduces the runtime and number of distance computations for the iORCA algorithm by 14.59% and 15.73%, respectively. Even compared to the new high-performance algorithm ADPOD, the PCV algorithm achieves 1.41% and 0.45% reductions. Notably, the non-outlier exclusion for the first data block in the dataset is significantly improved, with an exclusion rate of up to 36.5%, leading to a 23.54% reduction in detection time for that data block. While demonstrating excellent results, the PCV algorithm maintains the data type generality of metric space algorithms.

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