CAJ | 학술논문

数据发掘是人工智能和数据库研究的新兴领域，在很多领域有着卓有成效的应用。数据聚类或分割就是其中的一种重要的数据发掘应用。传统聚类方法存在的一个问题是要求分析人员定义距离函数，而距离函数并不是总能获得。在本文中，我们提出了一种新的无需距离函数的聚类方法。
수거발굴시인공지능화수거고연구적신흥영역，재흔다영역유착탁유성효적응용。수거취류혹분할취시기중적일충중요적수거발굴응용。전통취류방법존재적일개문제시요구분석인원정의거리함수，이거리함수병불시총능획득。재본문중，아문제출료일충신적무수거리함수적취류방법。
Data mining has been recognized as a new area for artificial intelligence and database research, and found its profitable applications in many areas. Clustering or segmentation of data is an important data mining application. One of the problems with traditional clustering methods is that they require the analyst to define distance functions that are not always available. In this paper, we propose a new method for clustering without distance functions.