Department of Management and Informatics, Silesian University of Technology, Katowice, Krasinskiego 8, Poland.
Neural Netw. 2011 Oct;24(8):824-30. doi: 10.1016/j.neunet.2011.05.013. Epub 2011 Jun 17.
Crisp and fuzzy-logic rules are used for comprehensible representation of data, but rules based on similarity to prototypes are equally useful and much less known. Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with the Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ depends highly on proper initialization of prototypes and the optimization mechanism. This paper introduces prototype initialization based on context dependent clustering and modification of the LVQ cost function that utilizes additional information about class-dependent distribution of training vectors. This approach is illustrated on several benchmark datasets, finding simple and accurate models of data in the form of prototype-based rules.
清晰和模糊逻辑规则用于数据的可理解表示，但基于相似性到原型的规则同样有用，而且鲜为人知。基于相似性的方法属于最准确的数据挖掘方法之一。其中一大类方法基于实例选择和优化，其中学习向量量化 (LVQ) 算法是一个突出的例子。LVQ 的准确性高度依赖于原型的适当初始化和优化机制。本文介绍了基于上下文相关聚类的原型初始化和 LVQ 代价函数的修改，该方法利用了关于训练向量类相关分布的附加信息。该方法在几个基准数据集上进行了说明，以基于原型的规则形式找到了数据的简单而准确的模型。
