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  • CN 31-1691/TQ

xk-split:基于k-medoids的分裂式聚类算法

陈逸斐 虞慧群

陈逸斐, 虞慧群. xk-split:基于k-medoids的分裂式聚类算法[J]. 华东理工大学学报(自然科学版), 2017, (6): 849-854,862. doi: 10.14135/j.cnki.1006-3080.2017.06.015
引用本文: 陈逸斐, 虞慧群. xk-split:基于k-medoids的分裂式聚类算法[J]. 华东理工大学学报(自然科学版), 2017, (6): 849-854,862. doi: 10.14135/j.cnki.1006-3080.2017.06.015
CHEN Yi-fei, YU Hui-qun. xk-split:A Split Clustering Algorithm Bases on k-medoids[J]. Journal of East China University of Science and Technology, 2017, (6): 849-854,862. doi: 10.14135/j.cnki.1006-3080.2017.06.015
Citation: CHEN Yi-fei, YU Hui-qun. xk-split:A Split Clustering Algorithm Bases on k-medoids[J]. Journal of East China University of Science and Technology, 2017, (6): 849-854,862. doi: 10.14135/j.cnki.1006-3080.2017.06.015

xk-split:基于k-medoids的分裂式聚类算法

doi: 10.14135/j.cnki.1006-3080.2017.06.015

xk-split:A Split Clustering Algorithm Bases on k-medoids

  • 摘要: 近年来互联网数据规模呈爆炸式增长,如何对大数据进行分析已成为热门话题。然而,采集的数据很难直接用于分析,需要进行一定程度的预处理,以提高大数据质量。通过使用分裂式的迭代过程,可以逐步将数据集分裂为子集,避免了传统聚类算法聚类开始时需要确定集群数的限制,并降低了算法的时间复杂度。此外,通过基于阈值的噪声数据过滤,可以在迭代过程中剔除噪音数据,提升了聚类算法对脏数据的忍耐力。

     

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出版历程
  • 收稿日期:  2016-11-23
  • 刊出日期:  2017-12-28

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