Scholarlink

<p>Outlier detection is an important issue in data mining, which has a wide range of applications in medicine, economics, video search, and credit card fraud detection. Many outlier detection methods have recently been developed. Most of the existing methods act based on the distance or density. Since each of these methods has its inherent disadvantage, we proposed a method which has the advantages of both distance-based and density-based methods. The proposed method is inspired by the basic idea that outliers are usually more distant neighbors to their nearest neighbors. The proposed method consists of three different parts. Each of these parts considers the distance, density, or location of objects, and finally we reach an optimal and efficient algorithm by combining these parts. Our algorithm is based on <i>k</i> nearest neighbor; in addition, we also use another kind of adaptive and extended neighborhood in order to provide more accurate results. Furthermore, the proposed method is robust and has little sensitivity to changes in parameter <i>k</i>. Numerical experiments and comparing with well-known algorithms are performed on both synthetic and real datasets in order to prove the efficiency and robustness of the proposed method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A robust approach for outlier detection based on the ratio of number of reverse neighbors to neighbors

  • 期刊论文

  • 发表日期：

    2025年1月7日

查看全文
  • Reza Heydari-Gharaei,
  • Rasoul Sharifi,
  • Shima Kashef,
  • Hossein Nezamabadi-pour

摘要

Outlier detection is an important issue in data mining, which has a wide range of applications in medicine, economics, video search, and credit card fraud detection. Many outlier detection methods have recently been developed. Most of the existing methods act based on the distance or density. Since each of these methods has its inherent disadvantage, we proposed a method which has the advantages of both distance-based and density-based methods. The proposed method is inspired by the basic idea that outliers are usually more distant neighbors to their nearest neighbors. The proposed method consists of three different parts. Each of these parts considers the distance, density, or location of objects, and finally we reach an optimal and efficient algorithm by combining these parts. Our algorithm is based on k nearest neighbor; in addition, we also use another kind of adaptive and extended neighborhood in order to provide more accurate results. Furthermore, the proposed method is robust and has little sensitivity to changes in parameter k. Numerical experiments and comparing with well-known algorithms are performed on both synthetic and real datasets in order to prove the efficiency and robustness of the proposed method.