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

共 50 条

[1] Mining top-K frequent itemsets from data streams
Wong, Raymond Chi-Wing
Fu, Ada Wai-Chee
DATA MINING AND KNOWLEDGE DISCOVERY, 2006, 13 (02) : 193 - 217
[2] Mining top-K frequent itemsets from data streams
Raymond Chi-Wing Wong
Ada Wai-Chee Fu
Data Mining and Knowledge Discovery, 2006, 13 : 193 - 217
[3] Mining Top-k Frequent-regular Itemsets from Data Streams Based on Sliding Window Technique
Mesama, Tashinee
Amphawan, Komate
2018 5TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATICS: CONCEPTS, THEORY AND APPLICATIONS (ICAICTA 2018), 2018, : 224 - 230
[4] Mining top-k high utility patterns over data streams
Zihayat, Morteza
An, Aijun
INFORMATION SCIENCES, 2014, 285 : 138 - 161
[5] Interactive mining of top-K frequent closed itemsets from data streams
Li, Hua-Fu
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (07) : 10779 - 10788
[6] Using Bloom Filters for Mining Top-k Frequent Itemsets in Data Streams
Kim, Younghee
Cho, Kyungsoo
Yoon, Jaeyeol
Kim, Ieejoon
Kim, Ungmo
SECURE AND TRUST COMPUTING, DATA MANAGEMENT, AND APPLICATIONS, 2011, 186 : 209 - 216
[7] Mining top-k frequent closed itemsets over data streams using the sliding window model
Tsai, Pauray S. M.
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (10) : 6968 - 6973
[8] Mining Top-k Frequent-regular Itemsets from Incremental Transactional Database
Tagmatcha, Bandit
Amphawan, Komate
2018 5TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATICS: CONCEPTS, THEORY AND APPLICATIONS (ICAICTA 2018), 2018, : 231 - 237
[9] Mining top-k frequent patterns over data streams sliding window
Chen, Hui
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2014, 42 (01) : 111 - 131
[10] Mining Top-k Co-Occurrence Patterns across Multiple Streams
Amagata, Daichi
Hara, Takahiro
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2017, 29 (10) : 2249 - 2262

← 1 2 3 4 5 →