Weak Ratio Rules

Jiang, Baoqing; Hu, Xiaohua; Wei, Qing; Jin, Zhen; Han, Chong; Liang, Meng

doi:10.4018/jdwm.2011070103

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…(1) The dam monitoring parameter data are symbolized and divided into subseries. As the data is all numerical, it needs to be converted to the Boolean type supported by the Apriori algorithm [25]. The input length of the original data is truncated into a number of subseries using a sliding window.…”

Section: Apriori-based Temporal Correlation Analysis Between Dam Moni...mentioning

confidence: 99%

Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

Chen,

Wang,

Zhao

et al. 2023

Smart Mater. Struct.

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In dam monitoring, anomalous data is often removed directly by researchers. However, some anomalous data may be due to sudden changes in the state of the dam itself and should not be removed. In this study, anomalous data in dam monitoring is divided into two categories: anomalous error data caused by anomalies in the monitoring equipment, and anomalous warning data caused by sudden changes in the state of the dam itself. Then we propose a method for identifying and reconstructing anomalous data in dam monitoring that takes into account temporal correlation. This method is able to identify and retain anomalous warning data, while removing and reconstructing anomalous error data. To determine the temporal correlation between dam monitoring parameters (e.g. water level, horizontal displacement, etc), we use association rules, and to reconstruct the removed dam monitoring data in the case of an incomplete dataset, we propose a dam monitoring data reconstruction network (DMDRN) based on generative adversarial network. On this basis and in combination with the density-based spatial clustering of applications with noise algorithm, the types of anomalous data in dam monitoring are identified, and the anomalous error data is reconstructed based on DMDRN. Our approach has been successfully validated in two experiments to identify and reconstruct anomalous data at a particular dam in China.

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Section: Apriori-based Temporal Correlation Analysis Between Dam Moni...mentioning

confidence: 99%

Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

Chen,

Wang,

Zhao

et al. 2023

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…Based on the type of values handled by the rule, the association rule can be classified as Boolean association rule and quantitative association rule. The presence or absence of items is the only concern of Boolean association rule (Agrawal, Imielinski, & Swami, 1993;Jiang, Hu, Wei, Song, Han, & Liang, 2011;Prakash, Kumar, & Gupta, 2011). Market basket analysis is an example of Boolean association rule mining.…”

Section: Association Rule Miningmentioning

confidence: 99%

A Survey on Fuzzy Association Rule Mining

Kalia¹,

Dehuri

Ghosh

2013

International Journal of Data Warehousing and Mining

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Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

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“…In association rule based cache replacement method, the client access history is mined to get association rules and a confidence value (Jiang et al, 2011;Ashrafi et al, 2004, Tjioe & Taniar, 2005Kwok et al, 2002;. Other caching variables (such as modification rate, cache invalidation delay, data messages size and retrieval delay) of the resulting rules are used in computing the objective function (Ashrafi et al, 2007;Trasarti et al, 2011;Khan et al, 2011;Koh & Pears, 2011) to determine which data item it to be replaced.…”

Section: Related Workmentioning

confidence: 99%

Rule-Based Data Mining Cache Replacement Strategy

Haraty

Zeitouny

2013

International Journal of Data Warehousing and Mining

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Caching in mobile environments is used for improving bandwidth usage and reducing query delays among resource constrained devices. It is used in multitudes of approaches. Some rely on the server occasionally broadcasting reports to inform clients of updated data while others permit clients to access data whenever they need it. Caching can be constrained by size and bandwidth; thus, coming up with a good invalidation scheme is critical in improving cache consistency solutions. In this work, the authors propose a new hybrid cache invalidation scheme that makes use of data mining concepts. They also compare their scheme with other related strategies.

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Weak Ratio Rules

Cited by 9 publications

References 25 publications

Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

A Survey on Fuzzy Association Rule Mining

Rule-Based Data Mining Cache Replacement Strategy

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