Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection

NEUROCOMPUTING

卷:397

Article

Nouretdinov, Ilia¹  Gammerman, James²  Fontana, Matteo³  Rehal, Daljit² 

[1] Royal Holloway Univ London, Comp Learning Res Ctr, Egham, Surrey, England

[2] Centrica Plc, Windsor, England

[3] Politecn Milan, Dept Management Econ & Ind Engn, Milan, Italy

关键词: Clustering;    Conformal prediction;    Dendrograms;   

DOI  :  10.1016/j.neucom.2019.07.114

来源: Elsevier

【 摘 要 】

In this work we present a clustering technique called multi-level conformal clustering (MLCC). The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpinnings of MLCC, compare and contrast it with the hierarchical clustering algorithm, and then apply it to real world datasets to assess its performance. There are several advantages to using MLCC over more classical clustering techniques: Once a significance level has been set, MLCC is able to automatically select the number of clusters. Furthermore, thanks to the conformal prediction framework the resulting clustering model has a clear statistical meaning without any assumptions about the distribution of the data. This statistical robustness also allows us to perform clustering and anomaly detection simultaneously. Moreover, due to the flexibility of the conformal prediction framework, our algorithm can be used on top of many other machine learning algorithms. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free