{"title":"GBMOD: A granular-ball mean-shift outlier detector","authors":"","doi":"10.1016/j.patcog.2024.111115","DOIUrl":null,"url":null,"abstract":"<div><div>Outlier detection is a crucial data mining task involving identifying abnormal objects, errors, or emerging trends. Mean-shift-based outlier detection techniques evaluate the abnormality of an object by calculating the mean distance between the object and its <span><math><mi>k</mi></math></span>-nearest neighbors. However, in datasets with significant noise, the presence of noise in the <span><math><mi>k</mi></math></span>-nearest neighbors of some objects makes the model ineffective in detecting outliers. Additionally, the mean-shift outlier detection technique depends on finding the <span><math><mi>k</mi></math></span>-nearest neighbors of an object, which can be time-consuming. To address these issues, we propose a granular-ball computing-based mean-shift outlier detection method (GBMOD). Specifically, we first generate high-quality granular-balls to cover the data. By using the centers of granular-balls as anchors, the subsequent mean-shift process can effectively avoid the influence of noise points in the neighborhood. Then, outliers are detected based on the distance from the object to the displaced center of the granular-ball to which it belongs. Finally, the distance between the object and the shifted center of the granular-ball to which the object belongs is calculated, resulting in the outlier scores of objects. Subsequent experiments demonstrate the effectiveness, efficiency, and robustness of the method proposed in this paper.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320324008665","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Outlier detection is a crucial data mining task involving identifying abnormal objects, errors, or emerging trends. Mean-shift-based outlier detection techniques evaluate the abnormality of an object by calculating the mean distance between the object and its -nearest neighbors. However, in datasets with significant noise, the presence of noise in the -nearest neighbors of some objects makes the model ineffective in detecting outliers. Additionally, the mean-shift outlier detection technique depends on finding the -nearest neighbors of an object, which can be time-consuming. To address these issues, we propose a granular-ball computing-based mean-shift outlier detection method (GBMOD). Specifically, we first generate high-quality granular-balls to cover the data. By using the centers of granular-balls as anchors, the subsequent mean-shift process can effectively avoid the influence of noise points in the neighborhood. Then, outliers are detected based on the distance from the object to the displaced center of the granular-ball to which it belongs. Finally, the distance between the object and the shifted center of the granular-ball to which the object belongs is calculated, resulting in the outlier scores of objects. Subsequent experiments demonstrate the effectiveness, efficiency, and robustness of the method proposed in this paper.
离群点检测是一项重要的数据挖掘任务,涉及识别异常对象、错误或新趋势。基于均值移动的离群点检测技术通过计算对象与其 k 近邻之间的平均距离来评估对象的异常性。然而,在存在大量噪声的数据集中,一些对象的 k 近邻中存在噪声,使得该模型无法有效检测异常值。此外,均值偏移离群点检测技术依赖于找到对象的 k 个近邻,这可能非常耗时。为了解决这些问题,我们提出了一种基于颗粒球计算的均值偏移离群点检测方法(GBMOD)。具体来说,我们首先生成高质量的颗粒球来覆盖数据。通过使用颗粒球的中心作为锚点,随后的均值转移过程可以有效避免邻域中噪声点的影响。然后,根据对象到其所属颗粒球的位移中心的距离来检测异常值。最后,计算对象与所属颗粒球的移位中心之间的距离,得出对象的离群值。随后的实验证明了本文所提方法的有效性、高效性和稳健性。
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.