CAJ | 학술논문

在现实世界中经常遇到混合数值属性和分类属性的数据， k-prototypes是聚类该类型数据的主要算法之一。针对现有混合属性聚类算法的不足，提出一种基于分布式质心和新差异测度的改进的 k-prototypes 算法。在新算法中，首先引入分布式质心来表示簇中的分类属性的簇中心，然后结合均值和分布式质心来表示混合属性的簇中心，并提出一种新的差异测度来计算数据对象与簇中心的距离，新差异测度考虑了不同属性在聚类过程中的重要性。在三个真实数据集上的仿真实验表明，与传统的聚类算法相比，本文算法的聚类精度要优于传统的聚类算法，从而验证了本文算法的有效性。
재현실세계중경상우도혼합수치속성화분류속성적수거， k-prototypes시취류해류형수거적주요산법지일。침대현유혼합속성취류산법적불족，제출일충기우분포식질심화신차이측도적개진적 k-prototypes 산법。재신산법중，수선인입분포식질심래표시족중적분류속성적족중심，연후결합균치화분포식질심래표시혼합속성적족중심，병제출일충신적차이측도래계산수거대상여족중심적거리，신차이측도고필료불동속성재취류과정중적중요성。재삼개진실수거집상적방진실험표명，여전통적취류산법상비，본문산법적취류정도요우우전통적취류산법，종이험증료본문산법적유효성。
Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principals for clustering this type of data objects. An improved k-prototypes algorithm is proposed to cluster mixed data in this paper. In our method, the concept of the distribution centroid is introduced for representing the prototype of categorical attributes in a cluster. Then we combine both mean with distribution centroid to represent the prototype of the cluster with mixed attributes, and thus propose a new measure to calculate the dissimilarity between data objects and prototypes of clusters. This measure takes into account the significance of different attributes towards the clustering process. Finally, we present out algorithm for clustering mixed data, and the performance of our method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional clustering algorithm.