基于设计规范距离（DSD）的K-均值工程材料数据库聚类性能评价
A Novel Design Specification Distance(DSD) Based K-Mean Clustering Performace Evluation on Engineering Materials Database
摘要：
将数据组织成语义上更有意义的数据是理解和学习的基本模式之一。聚类分析是对理解方法和学习算法的形式化研究。K-均值聚类算法是最基本、最简单的聚类算法之一。当没有关于数据集分布的先验知识时，K-mean是初始聚类数聚类的首选。本文将一种新的距离度量--设计规范（DS）距离度量函数与K-均值聚类算法相结合，以提高聚类精度。在P = 1.525时，采用提出的距离度量的K-means算法最大化聚类准确率为99.98%，这是通过迭代过程确定的。比较了K-均值算法下的设计规范（DS）距离度量函数与其他标准距离函数（欧几里德、平方欧几里德、城市街区和Chebshew相似性度量）的性能，并在工程材料数据库上对所提方法进行了评价。聚类分析和孤立点分析实验表明，这是一个很好的改善所提出的方法的性能。
英文摘要：
Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning. Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms. When there is no prior knowledge about the distribution of data sets, K-mean is the first choice for clustering with an initial number of clusters. In this paper a novel distance metric called Design Specification (DS) distance measure function is integrated with K-mean clustering algorithm to improve cluster accuracy. The K-means algorithm with proposed distance measure maximizes the cluster accuracy to 99.98% at P = 1.525, which is determined through the iterative procedure. The performance of Design Specification (DS) distance measure function with K - mean algorithm is compared with the performances of other standard distance functions such as Euclidian, squared Euclidean, City Block, and Chebshew similarity measures deployed with K-mean algorithm.The proposed method is evaluated on the engineering materials database. The experiments on cluster analysis and the outlier profiling show that these is an excellent improvement in the performance of the proposed method.
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。
