计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
4期
190-195
,共6页
近邻传播%谱聚类%密度敏感距离%层次化
近鄰傳播%譜聚類%密度敏感距離%層次化
근린전파%보취류%밀도민감거리%층차화
affinity propagation%spectral clustering%density-sensitive distance%hierarchical
以密度敏感距离作为相似性测度,结合近邻传播聚类算法和谱聚类算法,提出了一种密度敏感的层次化聚类算法。算法以密度敏感距离为相似度,多次应用近邻传播算法在数据集中选取一些“可能的类代表点”;用谱聚类算法将“可能的类代表点”再聚类得到“最终的类代表点”;每个数据点根据其类代表点的类标签信息找到自己的类标签。实验结果表明,该算法在处理时间、内存占用率和聚类错误率上都优于传统的近邻传播算法和谱聚类算法。
以密度敏感距離作為相似性測度,結閤近鄰傳播聚類算法和譜聚類算法,提齣瞭一種密度敏感的層次化聚類算法。算法以密度敏感距離為相似度,多次應用近鄰傳播算法在數據集中選取一些“可能的類代錶點”;用譜聚類算法將“可能的類代錶點”再聚類得到“最終的類代錶點”;每箇數據點根據其類代錶點的類標籤信息找到自己的類標籤。實驗結果錶明,該算法在處理時間、內存佔用率和聚類錯誤率上都優于傳統的近鄰傳播算法和譜聚類算法。
이밀도민감거리작위상사성측도,결합근린전파취류산법화보취류산법,제출료일충밀도민감적층차화취류산법。산법이밀도민감거리위상사도,다차응용근린전파산법재수거집중선취일사“가능적류대표점”;용보취류산법장“가능적류대표점”재취류득도“최종적류대표점”;매개수거점근거기류대표점적류표첨신식조도자기적류표첨。실험결과표명,해산법재처리시간、내존점용솔화취류착오솔상도우우전통적근린전파산법화보취류산법。
A hierarchical clustering algorithm based on density-sensitive distance which combined with Affinity Propagation (AP)algorithm and spectral clustering algorithm is proposed. Some“possible exemplars”are selected in the datasets by considering density-sensitive distance as similarity measure and repeatedly using AP algorithm;Applying the spectral clus-tering algorithm in the“possible exemplars”, the“final exemplars”are obtained; Each data points are assigned through the labels of their corresponding representative exemplars. Experimental results demonstrate that the algorithm outperforms the original AP algorithm and spectral clustering algorithm in terms of speed, memory usage, and clustering error rate.