Mining top-k regular episodes from sensor streams

被引：4

作者：

Amphawan, Komate ^{[1
]}

Soulas, Julie ^{[2
]}

Lenca, Philippe ^{[2
]}

机构：

[1] Burapha Univ, Fac Informat, Computat Innovat Lab, Chon Buri 20131, Thailand

[2] Inst Mines Telecom, Telecom Bretagne UMR Lab STICC 6285, F-29238 Brest 3, France

来源：

7TH INTERNATIONAL CONFERENCE ON ADVANCES IN INFORMATION TECHNOLOGY | 2015年 / 69卷

关键词：

Data mining; Activities of Daily Living; Episode discovery; Data stream; Sliding window; Regularity; FREQUENT; PATTERNS;

D O I：

10.1016/j.procs.2015.10.008

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The monitoring of human activities plays an important role in health-care applications and for the data mining community. Existing approaches work on activities recognition occurring in sensor data streams. However, regular behaviors have not been studied. Thus, we here introduce a new approach to discover top-k most regular episodes from sensors streams, TKRES. The top-k approach allows us to control the size of the output, thus preventing overwhelming result analysis for the supervisor. TKRES is based on the use of a simple top-k list and a k-tree structure for maintaining the top-k episodes and their occurrence information. We also investigate and report the performances of TKRES on two real-life smart home datasets. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：76 / 85

页数：10

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