Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

作     者：Shinjin Kang Soo Kyun Kim 

作者机构：School of GamesHongik UniversitySejong30016Korea Department of Computer EngineeringJeju National UniversityJeju63243Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3725-3736页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Outlier detection trajectory clustering behavior analysis app data smartphone 

摘      要：In this study,we propose a low-cost system that can detect the space outlier utilization of residents in an indoor *** focus on the users’app usage to analyze unusual behavior,especially in indoor *** is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently *** system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier *** density-based spatial clustering of applications with noise(DBSCAN)algorithm was applied for effective singular movement *** analyze high-level mobile phone usage,the t-distributed stochastic neighbor embedding(t-SNE)algorithm was *** two clustering algorithms can effectively detect outlier behaviors in terms of movement and app usage in indoor *** experimental results showed that our system enables effective spatial behavioral analysis at a low cost when applied to logs collected in actual living ***,large volumes of data required for outlier detection can be easily *** system can automatically detect the unusual behavior of a user in an indoor *** particular,this study aims to reflect the recent trend of the increasing use of smartphones in indoor spaces to the behavioral analysis.