An improved semisupervised outlier detection algorithm based on adaptive feature weighted clustering
type: Item
instance of: scholarly article
title: An improved semisupervised outlier detection algorithm based on adaptive feature weighted clustering
zbMATH Open document ID: 1400.62130
DOI: 10.1155/2016/6394253
author: Jinhong Yang
author: Tingquan Deng
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62F35
zbMATH DE Number: 6953441
Wikidata QID: Q59140879
describes a project that uses: UCI-ml
describes a project that uses: LOF
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/6394253
OpenAlex ID: W2567596697
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description: scientific article; zbMATH DE number 6953441