[论文解读] Iterative Optimization and Simplification of Hierarchical Clusterings

Douglas Fisher|arXiv (Cornell University)|Apr 1, 1996

Data Mining Algorithms and Applications参考文献 9被引用 9

一句话总结

本文提出了一种用于分层聚类的迭代优化与简化框架，结合了低成本的初始聚类与基于重采样剪枝和基于属性选择的目标函数的后台优化。该方法提升了聚类质量与简洁性，减轻了后续分析负担，同时在模式补全任务中保持了高性能。

ABSTRACT

Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts -- often according to task-specific criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to `externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.

研究动机与目标

• 解决分层聚类系统中聚类质量与计算成本之间的平衡挑战。
• 通过简化聚类结构而不牺牲实用性，减轻聚类后分析负担。
• 提出两阶段方法：初始聚类后进行后台迭代优化。
• 将监督学习中常见的基于重采样的剪枝策略整合到聚类简化中。
• 基于属性选择度量评估目标函数，以提升聚类性能与简洁性。

提出的方法

• 采用低成本策略生成初步聚类，以实现快速初始分析。
• 通过反复修改聚类结构来优化目标函数，实现迭代优化。
• 将监督学习中常用的基于重采样的剪枝技术适配用于简化分层聚类。
• 利用决策树归纳中的度量（如信息增益）定义目标函数，以指导优化过程。
• 采用外部性能任务——模式补全错误率——对外部评估聚类的实用性。
• 在后台执行优化，以在不阻塞初始分析的前提下细化聚类，实现持续改进。

实验结果

研究问题

• RQ1 迭代优化是否能在保持计算高效的同时显著提升分层聚类的质量？
• RQ2 基于重采样的剪枝策略在不降低性能的前提下，对简化分层聚类结构有多大的有效性？
• RQ3 基于属性选择度量的目标函数中，哪一种最能平衡聚类质量与简洁性？
• RQ4 后台迭代优化在不延迟初始分析的前提下，能在多大程度上提升聚类实用性？
• RQ5 在不同优化策略下，模式补全错误率与聚类质量之间的相关性如何？

主要发现

• 迭代优化策略显著提升了初始聚类的聚类质量，尤其在使用信息论目标函数引导时效果更明显。
• 基于重采样的剪枝能有效简化聚类结构，降低复杂度，减轻聚类后分析负担。
• 基于属性选择度量的目标函数（如信息增益）在平衡质量与简洁性方面优于标准准则。
• 两阶段方法——先进行初始聚类，再进行后台优化——实现了快速初始洞察，同时持续提升结果质量。
• 该方法在模式补全任务中表现优异，优化后错误率有显著降低。
• 该框架通过结合计算效率与高质量、可分析的聚类输出，展现出实际应用价值。