Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint)

Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint)

Yang Cao1 , Ye Zhu1 , Kai Ming Ting2 , Flora D. Salim3 , Hong Xian Li4 , Luxing Yang4 and Gang Li4

1Deakin University, Geelong, Australia

2Nanjing University, Nanjing, China

3University of New South Wales, Kensington, Australia

4Deakin University, Geelong, Australia

charles.cao@ieee.org, ye.zhu@ieee.org, tingkm@nju.edu.cn, flora.salim@unsw.edu.au, hong.li@deakin.edu.au, y.luxing@deakin.edu.au, gang.li@deakin.edu.au

Abstract Reprint. This is an abstract reprint of a journal by [Cao et al., 2024].

Abstract

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD)methods have been proposed recently to identify those changes, they still suffer from missing subtle changes, poor scalability, or/and sensitivity to outliers. To meet these challenges,we are the first to generalise the CPD problem as a special case of the Change-Interval Detection (CID) problem. Then we propose a CID method, named iCID, based on are cent Isolation Distributional Kernel (IDK). iCID identifies the change interval if there is a high dissimilarity score between two non-homogeneous temporal adjacent intervals. The datadependent property and finite feature map of IDK enabled iCID to efficiently identify various types of change-points in data streams with the tolerance of outliers. Moreover,the proposed online and offline versions of iCID have the ability to optimise key parameter settings. The effectiveness and efficiency of iCID have been systematically verified on both synthetic and real-world datasets