Research Article
Activity Behavior Pattern Mining and Recognition
@INPROCEEDINGS{10.1007/978-3-030-89814-4_48, author={Ling Song and Hongxin Liu and Shunming Lyu and Yi Liu and Xiaofei Niu and Xinfeng Liu and Mosu Xu}, title={Activity Behavior Pattern Mining and Recognition}, proceedings={Mobile Multimedia Communications. 14th EAI International Conference, Mobimedia 2021, Virtual Event, July 23-25, 2021, Proceedings}, proceedings_a={MOBIMEDIA}, year={2021}, month={11}, keywords={Activity behavior and socio-demographic pattern mining Activity behavior and socio-demographic pattern recognition Clustering analysis and mining Activity sequence similarity Activity behavior similarity}, doi={10.1007/978-3-030-89814-4_48} }- Ling Song
Hongxin Liu
Shunming Lyu
Yi Liu
Xiaofei Niu
Xinfeng Liu
Mosu Xu
Year: 2021
Activity Behavior Pattern Mining and Recognition
MOBIMEDIA
Springer
DOI: 10.1007/978-3-030-89814-4_48
Abstract
As human activity in mobile environments is facing with an ever-increasing range of data, therefore, a deeper understanding of the human activity behavior pattern and recognition is of important research significance. However, human activity behavior that consists of a series of complex spatiotemporal processes is hard to model. In this paper, we develop a platform to do pattern mining and recognition, the main work is as follows: (1) For comparing activity behavior, similarity matrix is computed based on activity intersection, temporal connections, spatial intersection, participant intersection and activity sequence comparison. (2) For calculating activity sequence similarity, an algorithm with O(p(m–p)) is proposed by line segment tree, greedy algorithm and dynamic programming. (3) Activity behavior pattern and socio-demographic pattern are derived by clustering analysis and mining. (4) Pattern is recognized under the inter-dependency relationship between activity behavior pattern and socio-demographic pattern.
