CAJ | 학술논문

基于关系型数据库管理系统( DBMS)的数据挖掘算法对数据库程序员来说是一个很重要的问题.这里介绍了利用SQL实现的基于关系数据库管理系统的K-means聚类算法,将简单的K-means计算转化为SQL.实验证明,提出的K-means聚类算法可以对大型数据集进行聚类.将K-means算法分别用SQL和C+ +实现,比较相关的速度和可伸缩性,并且研究了在DBMS外输出数据集的时间.实验表明,SQL对于小型数据集还是很有效的,但对于大型数据集效率较低,而输出次数对于C+ +成为了一个瓶颈.
기우관계형수거고관리계통( DBMS)적수거알굴산법대수거고정서원래설시일개흔중요적문제.저리개소료이용SQL실현적기우관계수거고관리계통적K-means취류산법,장간단적K-means계산전화위SQL.실험증명,제출적K-means취류산법가이대대형수거집진행취류.장K-means산법분별용SQL화C+ +실현,비교상관적속도화가신축성,병차연구료재DBMS외수출수거집적시간.실험표명,SQL대우소형수거집환시흔유효적,단대우대형수거집효솔교저,이수출차수대우C+ +성위료일개병경.
Data mining algorithms with a relational DBMS is an important problem for database programmers. We introduce SQL implementations of K-means clustering algorithm to integrate it with a relational DBMS, translate K-means computations into SQL. We experimentally show the proposed K-means can cluster large data sets. We compare K-means implementations in SQL and C+ + with respect to speed and scalability and we also study the time to export data sets outside of the DBMS. Experiments show that SQL overhead is significant for small data sets,but relatively low for large data sets,whereas export times become a bottleneck for C+ +.