摘要
将共享近邻(SNN)算法中的近邻概念具体化,为K-均值算法(KMI)设计了一种初始类心选取方案,并把改进的共享近邻算法(MSNN)和改进的K-均值算法(MKMI)合并到一起形成一种不需外界干预的综合聚类算法(CCA).用两组语音数据进行了比较试验,结果表明,MKMI和CCA的性能比KMI的分别高57％和108％;MSNN是一种比较有效的粗分类算法。
The concept of near-ness in the shared nearest neighbors(SNN) is given an embodiment and an algorithm for determining initial cluster centers in the K- means iteration (KMT) is designed. The modified SNN (MSNN) and the modified KMI (MKMI) are merged to from an unsupervised combined clustering algorithm (CCA). For comparison, the KMI, MKMI, and CCA were applied to classify two groups of speech data. The results show that the performances of the MKMI and CCA are respectively 57% and 108% higher than that of the KMI, and MSNN is a good pre-classifier.
出处
《北京理工大学学报》
EI
CAS
CSCD
1992年第3期37-42,共6页
Transactions of Beijing Institute of Technology
关键词
模式识别
聚类分析
语音模板
pattern recognition
cluster analysis/shared nearest neighbor, K-means
convergence
