Absolute and relative clustering

Authors:
Toshihiro Kamishima;Shotaro Akaho
Affiliations:
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
Venue:
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
Year:
2013

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Abstract

Research into (semi-)supervised clustering has been increasing. Supervised clustering aims to group similar data that are partially guided by the user's supervision. In this supervised clustering, there are many choices for formalization. For example, as a type of supervision, one can adopt labels of data points, must/cannot links, and so on. Given a real clustering task, such as grouping documents or image segmentation, users must confront the question "How should we mathematically formalize our task?" To help answer this question, we propose the classification of real clusterings into absolute and relative clusterings, which are defined based on the relationship between the resultant partition and the data set to be clustered. This categorization can be exploited to choose a type of task formalization.