Chi tiết| Tên | Evidential seed-based semi-supervised clustering |
| Lĩnh vực | Tin học |
| Tác giả | Violaine Antoine, Nicolas Labroche, Vũ Việt Vũ |
| Nhà xuất bản / Tạp chí | Proceeding of the 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems Năm 2014 |
| Số hiệu ISSN/ISBN | |
| Tóm tắt nội dung | |
Evidential clustering algorithms produce credal
partitions that enhance the concepts of hard, fuzzy or possibilistic
partitions to represent all assignments ranging from complete
ignorance to total certainty. This paper introduces the first
semi-supervised extension of the evidential c-means clustering
algorithm that can benefit from the introduction of a small set of
labeled data (or seeds). Experiments conducted on real datasets
show that the introduction of seeds can lead to a significant
increase in clustering accuracy compared to a traditional evidential
clustering algorithm as well as a decrease in the number
of iterations to convergence.
Evidential clustering algorithms produce credalpartitions that enhance the concepts of hard, fuzzy or possibilisticpartitions to represent all assignments ranging from completeignorance to total certainty. This paper introduces the firstsemi-supervised extension of the evidential c-means clusteringalgorithm that can benefit from the introduction of a small set oflabeled data (or seeds). Experiments conducted on real datasetsshow that the introduction of seeds can lead to a significantincrease in clustering accuracy compared to a traditional evidentialclustering algorithm as well as a decrease in the numberof iterations to convergence.
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