递阶规划的迭代优化与简化
Iterative Optimization and Simplification of Hierarchical Clusterings
摘要：
聚类通常用于发现数据中的结构。聚类系统在用于评价聚类质量的目标函数和用于搜索聚类空间的控制策略上有所不同。理想情况下，搜索策略应该始终构建高质量的聚类，但计算成本也很低。在一般情况下，我们不能有它的两种方式，但我们可以分区搜索，使系统廉价地构建一个“试探性的”聚类进行初步检查，其次是迭代优化，继续在后台搜索改进的聚类。鉴于这一动机，我们评估一个廉价的策略来创建初始聚类，再加上几个控制策略的迭代优化，其中每一个重复修改初始聚类，以寻找一个更好的。其中一种方法作为聚类上下文中的迭代优化策略显得新颖。一旦构建了聚类，分析师就可以对其进行判断-通常是根据特定于任务的标准。一些作者已经抽象出这些标准，并提出了一个通用的性能任务，类似于模式完成，其中完成模式的出错率是用来“外部”判断聚类效用。鉴于此性能的任务，我们适应基于重采样的修剪策略所使用的监督学习系统的任务，简化层次聚类，从而有望减轻后聚类分析。最后，我们提出了一些目标函数，决策树归纳的属性选择措施的基础上，可能会表现良好的出错率和简单性的尺寸。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
