  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 a recent
Isolation Distributional Kernel (IDK). iCID identifies the change interval if
there is a high dissimilarity score between two non-homogeneous temporal
adjacent intervals. The data-dependent 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.
