Contextual Hausdorff dissimilarity for multi-instance clustering
Ying Chen, Ou Wu · 2012
The multi-instance clustering problem has been emerging in kinds of applications. A straightforward solution is to adapt the classical single-instance clustering algorithms such as k-mediods to the setting of it. In this way, the essential step is the dissimilarity measurement between multi-instance bags. Traditional distances fail to capture the differences between bags. This paper proposes a new type of bag dissimilarity, namely, contextual Hausdorff dissimilarity (CHD). Then a multi-instance clustering algorithm based on CHD is introduced. Experimental results on both synthetic data and real-world data sets show that the proposed CHD outperforms the traditional Hausdorff dissimilarity.