DenMune: Density peak based clustering using mutual nearest neighbors

PATTERN RECOGNITION

卷:109

Article

Abbas, Mohamed¹  El-Zoghabi, Adel¹  Shoukry, Amin^2,3 

[1] Inst Grad Studies & Res, Informat Technol, Alexandria, Egypt

[2] Egypt Japan Univ Sci & Technol, Comp Sci & Engn, Alexandria, Egypt

[3] Fac Engn, Comp & Syst Engn Dept, Alexandria, Egypt

关键词: Clustering;    Mutual neighbors;    Dimensionality reduction;    Arbitrary shapes;    Pattern recognition;    Nearest neighbors;    Density peak;   

DOI  :  10.1016/j.patcog.2020.107589

来源: Elsevier

【 摘 要 】

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm DenMune is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K , where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K . Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free