Active semisupervised clustering algorithm with label propagation for imbalanced and multidensity datasets
type: Item
instance of: scholarly article
title: Active semisupervised clustering algorithm with label propagation for imbalanced and multidensity datasets
zbMATH Open document ID: 1299.62051
author: Mingwei Leng
author: Jianjun Cheng
author: Jinjin Wang
author: Zhengquan Zhang
author: Hanhai Zhou
author: Xiaoyun Chen
published in: Mathematical Problems in Engineering
publication date: 2014-11-24T00:00:00Z
review text: Summary: The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 6372563
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 6372563