CAJ | 학술논문

为估计数据集的聚类数目及获得较好的聚类性能,提出了一种基于灰关联测度的分裂式层次聚类算法.该算法用灰关联测度衡最数据对象之间的相似程度,以基于密度扩展的方式自顶向下分裂成不同层次的数据集划分;然后,根据灰关联测度定义聚类有效性指标;最后将有效性指标曲线极值点对应的聚类划分用于估计最佳聚类数目.实际数据和合成数据集的实验表明,与FCM聚类相比,该算法的聚类正确率平均提高3.7%,并且能够识别任意形状的簇.
위고계수거집적취류수목급획득교호적취류성능,제출료일충기우회관련측도적분렬식층차취류산법.해산법용회관련측도형최수거대상지간적상사정도,이기우밀도확전적방식자정향하분렬성불동층차적수거집화분;연후,근거회관련측도정의취류유효성지표;최후장유효성지표곡선겁치점대응적취류화분용우고계최가취류수목.실제수거화합성수거집적실험표명,여FCM취류상비,해산법적취류정학솔평균제고3.7%,병차능구식별임의형상적족.
To estimate cluster number and achieve a better clustering performance,a divisive hierarchical clustering algorithm based on grey relational measure was proposed.In this algorithm,the grey relational measure is used to measure the degree of similarity between data sets.On the basis of the way of density-based extension,the algorithm divisively generates hierarchical partitions of dataset.And then the clustering validity index is defined based on the grey relational measure.The partitions corresponding to the extremum of the validity index curve are used to estimate the number of clusters finally.Computer simulation on real and synthesis data sets shows that compared with the FCM (fuzzy C-means)algorithm,the proposed algorithm has a 3.7%improvement in average clustering correct rate and is good for arbitrary-shaped clusters.