@inproceedings{d2e0228ab2a04a0f9a436622cc68f42f,
title = "Toward mining user traversal patterns in the indoor environment",
abstract = "We in this paper explore a new mining paradigm, called Indoor Traversal Patterns (abbreviated as ITP), to discover user traversal behavior in the mall-like indoor environment. The ITP algorithm can identify user traversal sequences from uncertain user itineraries with the RFID-based indoor positioning technology. Note that it is a highly challenging issue in the indoor environment to retrieve the precise locations in the indoor environment. Since previous works on mining user moving patterns usually rely on the precise spatiotemporal information from GPS signals, it is difficult to apply similar approaches to discover user traversal behavior in the indoor environment. We therefore develop a framework to transform the RFID antenna data to uncertain user traversal transactions, and further diminish the uncertainty before mining the indoor traversal patterns. Our experimental studies show that the proposed ITP algorithm can effectively overcome the impact from location uncertainty and discover high-quality traversal patterns, to provide insightful observation for marketing decision.",
author = "Teng, {Shan Yun} and Chung, {Tzu Yuan} and Chuang, {Kun Ta} and Ku, {Wei Shinn}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 ; Conference date: 13-05-2014 Through 16-05-2014",
year = "2014",
doi = "10.1007/978-3-319-13186-3_60",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "677--688",
editor = "Wen-Chih Peng and Haixun Wang and Zhi-Hua Zhou and Ho, {Tu Bao} and Tseng, {Vincent S.} and Chen, {Arbee L.P.} and James Bailey",
booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops",
address = "Germany",
}