CAJ | 학술논문

针对现有的 Neural-Gas 算法进行改进，提出了一种新的聚类算法。改进之处在于：一个点对一个簇的质心的影响程度取决于该点到其他更近的簇的质心的距离值，而不仅仅是点与簇质心间距离值按大小排列次序的序号。在几个数据集上的实验结果表明，该算法在熵、纯度、F1值、rand index、规范化互信息 NMI 等五个指标上优于 K-means 算法、Neural-Gas 算法等其他几种聚类算法，该算法是一种较好较快的算法。
침대현유적 Neural-Gas 산법진행개진，제출료일충신적취류산법。개진지처재우：일개점대일개족적질심적영향정도취결우해점도기타경근적족적질심적거리치，이불부부시점여족질심간거리치안대소배렬차서적서호。재궤개수거집상적실험결과표명，해산법재적、순도、F1치、rand index、규범화호신식 NMI 등오개지표상우우 K-means 산법、Neural-Gas 산법등기타궤충취류산법，해산법시일충교호교쾌적산법。
This paper proposed a new clustering algorithm by improving existed algorithm Neural-Gas.The improvement was that the degree of influence of a point on a cluster centroid depended on the distance values between this point and the other more near cluster centroids,not just the sequence number arranged in the order of the distance value between this point and cluster centroids.Experimental results on several data sets show that on five indicators such as entropy,purity,F1 value,rand index and normalized mutual information this improved algorithm surpasses K-means,Neural-Gas and several other algorithms. The conclusion is that this improved algorithm is a better and faster algorithm.