A Hierarchical Method for Determining the Number of Clusters
- 软件学院－已发表论文 
确定数据集的聚类数目是聚类分析中一项基础性的难题.常用的trail-and-error方法通常依赖于特定的聚类算法,且在大型数据集上计算效率欠佳.提出一种基于层次思想的计算方法,不需要对数据集进行反复聚类,它首先扫描数据集获得CF(clusteringfeature,聚类特征)统计值,然后自底向上地生成不同层次的数据集划分,增量地构建一条关于不同层次划分的聚类质量曲线;曲线极值点所对应的划分用于估计最佳的聚类数目.另外,还提出一种新的聚类有效性指标用于衡量不同划分的聚类质量.该指标着重于簇的几何结构且独立于具体的聚类算法,能够识别噪声和复杂形状的簇.在实际数据和合成数据上的实验结果表明,新方法的性能优于新近提出的其他指标,同时大幅度提高了计算效率.A fundamental and difficult problem in cluster analysis is the determination of the "true" number of clusters in a dataset. The common trail-and-error method generally depends on certain clustering algorithms and is inefficient when processing large datasets. In this paper, a hierarchical method is proposed to get rid of repeatedly clustering on large datasets. The method firstly obtains the CF (clustering feature) via scanning the dataset and agglomerative generates the hierarchical partitions of dataset, then a curve of the clustering quality w.r.t the varying partitions is incrementally constructed. The partitions corresponding to the extremum of the curve is used to estimate the number of clusters finally. A new validity index is also presented to quantify the clustering quality, which is independent of clustering algorithm and emphasis on the geometric features of clusters, handling efficiently the noisy data and arbitrary shaped clusters. Experimental results on both real world and synthesis datasets demonstrate that the new method outperforms the recently published approaches, while the efficiency is significantly improved.