Clustering methods and their uses in computational chemistry
Clustering methods and their uses in computational chemistry
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DOI：
10.1002/0471433519.ch1
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发表时间：
2002-01-01
期刊：
影响因子：
--
通讯作者：
Barnard, JM
中科院分区：
文献类型：
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作者：
Downs, GM;Barnard, JM
聚类是一种数据分析技术，当应用于一组异质项目时，识别由给定模型或相似性度量定义的同质子组。在聚类的众多用途中，聚类方法越来越受到关注的主要原因是它们在药物发现相关的化学结构组合库的选择和设计中的应用。聚类的一个特点是该过程是无监督的，也就是说，没有预定义的聚类试图重现的分组。监督学习的任务是在给定的输入和输出之间建立关系，以便根据新输入预测输出，而非监督学习的任务是揭示输入数据的潜在分布。因此，聚类由相关的监督分类过程来补充，其中为项目分配应用于预定义组的标签：示例包括递归分区、朴素贝叶斯分析和K最近邻选择。聚类是一种探索性数据分析技术，越来越多地用于中高维大型数据集的初步分析，作为选择，多样性分析和数据简化的方法。本章回顾了用于化学分析的主要聚类方法
Clustering is a data analysis technique that, when applied to a set of heterogeneous items, identifies homogeneous subgroups as defined by a given model or measure of similarity. Of the many uses of clustering, a prime motivation for the increasing interest in clustering methods is their use in the selection and design of combinatorial libraries of chemical structures pertinent to pharmaceutical discovery.One feature of clustering is that the process is unsupervised, that is, there is no predefined grouping that the clustering seeks to reproduce. In contrast to supervised learning, where the task is to establish relationships between given inputs and outputs to enable prediction of the output from new inputs, in unsupervised learning only the inputs are available and the task is to reveal aspects of the underlying distribution of the input data. Clustering is thus complemented by the related supervised process of classification, in which items are assigned labels applied to predefined groups: examples include recursive partitioning, naıve Bayesian analysis, and K nearest-neighbor selection. Clustering is a technique for exploratory data analysis and is used increasingly in preliminary analyses of large data sets of medium and high dimensionality as a method of selection, diversity analysis, and data reduction. This chapter reviews the main clustering methods that are used for analyzing chemical