【摘要】 针对传统范例提取算法随范例数增加而效率下降快的缺点 ,结合基于选择的 CL ARA聚类方法和 NCL聚类算法的优点 ,给出了一种有效的无监督聚类学习算法 .通过实验表明 ,该算法能在无监督下对范例进行准确归类 ,将它用于 CBR的范例提取中 ,能大大地提高范例提取的速度和质量更多还原

【Abstract】 In this paper,a new algorithm for unsupervised Clustering analysis is proposed,This new clustering method absorbs merits of the selection-based CLARA clustering algorithm and the new NCL clustering algorithm.It overcomes the low efficiency of traditional case retrieval algorithms when CB gets very large.The experiments show that the new algorithm can cluster cases correctly and it is unsupervised,this method can improve the speed and quality of the case base retrieval process greatly.更多还原