Abstract
Hierarchical clustering algorithm is efficient in reducing the bytes needed to describe the original information while preserving the original information structure. Information Bottleneck (IB) theory is a hierarchical clustering framework derivative from the information theory. Agglomerative Information Bottleneck (AIB) algorithm is a suboptimal agglomerative clustering procedure designed for optimizing the original computation-exhausted IB algorithm. But the Monte-Carlo simulation formula which is widely adopted for distortion measures in AIB algorithm is problematic. This paper testified that there being a contradiction between the adopted Monte-Carlo formula and IB principle. Extending special distortion measures to common distances, the paper also present several proposals. And Experiments show their efficiency and availability.
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