Knowledge discovery of consensus and conflict interval-based temporal patterns: A novel group decision approach

Huang, Tony Cheng-Kui; Yang, Pu-Tai; Hsieh, Kun-Yu

doi:10.1016/j.knosys.2017.11.002

Cited by 5 publications

(1 citation statement)

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“…Traditionally, knowledge discovery (data mining) in databases (KDD) [27] is a process to extract useful knowledge from a dataset or a database. Recently, KDD techniques have been extended to the WSNs field (called knowledge discovery in WSNs (KDW) [10]), and have attracted significant attention in many application areas [2], such as anomaly detection in railway data [28], relational temporal data mining [3], [29], [30] using Allen's temporal interval logic [31], object tracking [4], [11], [32], prediction of the location (target) of a missed reported event [33], and outlier (abnormal event) detection [34].…”

Section: B Knowledge Discovery In Wsnsmentioning

confidence: 99%

Mining Associated Ranking Patterns from Wireless Sensor Networks

2016

International Conference on Engineering Technologies and Big Data Analytics (ETBDA’2016) Jan. 21-22, 2016 Bangkok (Thailand)

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Wireless Sensor Networks (WSNs) are complex networks consisting of many sensors which can detect and collect sensed data. Valuable information can be extracted from the sensed data of WSNs using data mining techniques, which provide unlimited possibilities of applications, and have been the object of an increasing number of studies in recent years. This study applies ranking sequence mining to WSNs. For example, if a sensor detects the following scenario during a time interval: A occurs three times; B occurs twice; C occurs four times, where A, B and C are events. The information, (C(4) > A(3) > B(2)), can be abstractly expressed as a ranking sequence (C ≻ A ≻ B), where "≻" indicates to the importance of the preceding item is greater than that of the succeeding item. To the best of our knowledge, there has been no research conducted on ranking pattern mining problems with WSNs. The objective of this study is to construct a novel model to discover interesting patterns from the sensed data of WSNs.

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