Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 107-115.doi: 10.6040/j.issn.1672-3961.0.2018.458

• Machine Learning & Data Mining • Previous Articles     Next Articles

Transfer fuzzy clustering based on self-constraint of multiple medoids

Jun QIN1(),Yuanpeng ZHANG1,2,*(),Yizhang JIANG2,Wenlong HANG3   

  1. 1. Department of Medical Informatics, Nantong University, Nantong 226001, Jiangsu, China
    2. School of Digital Media, Jiangnan University, Wuxi 214122, Jiangsu, China
    3. School of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, Jiangsu, China
  • Received:2018-10-29 Online:2019-04-20 Published:2019-04-19
  • Contact: Yuanpeng ZHANG E-mail:2432533512@qq.com;155297131@qq.com
  • Supported by:
    国家自然科学基金资助项目(81701793);南通市科技计划资助项目(MS12017016-2);江苏省社会科学基金资助项(18YSC009)

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Abstract:

Transfer clustering approaches derived from the fuzzy C-means (FCM) framework, which considered virtual centers from source domains as transfer knowledge, inherited the shortcomings of FCM. These methods were not robust to outliers and noises, and whose single cluster centers were not sufficient enough to capture the inner structures of clusters. To solve the problems, a transfer fuzzy clustering approach was proposed based on the self-constraint of multiple medoids. Prototype weights were introduced and assigned to each object to capture the inner structures of clusters. Such a weighting strategy could capture the inner structures of clusters more sufficiently and made the clustering more robust to outliers and noises; Furthermore, with the distribution of data in the source domain, the inner structure of data in the target domain was reconstructed, and the corresponding new structure was considered as the transfer knowledge to guide the clustering of the target domain. Relative to the use of single virtual center of each cluster as transfer knowledge, the updated inner structures of data in the target domain contained more knowledge. Experimental results demonstrated that the proposed approach achieved 0.674 5 and 0.608 4 improvements in terms of NMI and ARI on synthetic datasets and real-life datasets compared with introduced benchmarking approaches. Therefore, based on the transfer principle of the self-constraint of multiple medoids, the proposed clustering approach performed well in the transfer environment.

Key words: fuzzy clustering, transfer clustering, multiple exemplars, transfer learning, unsupervised learning

CLC Number: 

  • TP391

Fig.1

Transfer learning of PPKTFCM"

Table 1

Parameter settings of each approach"

算法 算法简介 参数设置
FCM 基于虚拟簇中心和“软”划分的模糊均值聚类算法 模糊指数m:[1.1, 1.2, …, 3]
AP和TAP AP聚类算法是基于数据点间的“信息传递”的一种聚类算法。TAP算法是AP算法的泛化,利用源域数据的统计特征(分布匹配迁移策略)以及源域数据和目标域数据之间的几何特征(实例保留迁移策略)来实现迁移聚类 最佳近邻个数:[1, 2, …, 7], λ1:[0.1, 0.2, …, 1], λ2:[1, 2, …, 10]
PPKTFCM 在FCM框架下提出的基于样本点与历史类中心点距离和极小规则以及隶属度变化极小规则的一种模糊迁移聚类算法 模糊指数m:[1.1, 1.2, …, 3], λ1:[0.1, 0.2, …, 10], λ2:[1, 2, …, 10]
退化的MMSC-TFC和MMSC-TFC 本研究提出的算法 λ1:[0.1, 0.2, …, 10], λ2:[1, 2, …, 10], $\boldsymbol{\epsilon}$=10-4

Fig.2

Synthetic datasets"

Table 2

Clustering results on D-T of the target domain"

算法 K=2 K=3
NMI ARI NMI ARI
FCM 0.246 2 0.086 2 0.796 7 0.822 1
AP 0.337 3 0.408 7 0.621 8 0.625 6
退化的MMSC-TFC 0.735 7 0.828 2 0.847 9 0.886 2
PPKTFCM 0.296 1 0.150 3 0.761 0 0.784 5
TAP 0.613 9 0.709 8 0.794 8 0.805 2
MMSC-TFC 0.867 4 0.912 1 1.000 0 1.000 0

Fig.3

Clustering results on the synthetic dataset"

Table 3

Sample composition of two transfer scenarios: source domain and target domain"

场景 数据集 大小 维数 簇个数
comp VS sci 源域 1 500 350 2
目标域 150 350
rec VS talk 源域 1 500 350 2
目标域 150 350

Table 4

Clustering results on real-life datasets"

算法 comp VS sci rec VS talk
NMI ARI NMI ARI
FCM 0.221 4 0.189 7 0.235 7 0.257 4
AP 0.489 2 0.501 1 0.202 0 0.688 5
退化的MMSC-TFC 0.732 2 0.801 2 0.712 5 0.875 2
PPKTFCM 0.723 6 0.789 6 0.892 2 0.911 4
TAP 0.795 1 0.775 4 0.823 0 0.938 7
MMSC-TFC 0.785 2 0.798 1 0.910 2 0.922 6
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