Some Question to Monte-Carlo Simulation in AIBAlgorithm

摘要

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.