Efficient mining of frequent episodes from complex sequences

Huang, Kuo-Yu; Chang, Chia‐Hui

doi:10.1016/j.is.2007.07.003

Cited by 82 publications

(53 citation statements)

References 22 publications

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“…Another exiting approach is based on the assumption that in an event sequence there are events at each time slot in terms of various intervals (hours, days, weeks, etc.) such sequences must satisfy more complex representation [9].…”

Section: State Of the Artmentioning

confidence: 99%

“…Incipient fault detection and analysis of failures is a recent topic of great interest for the development of predictive maintenance policies of the electrical system. For example, in [9] abnormal and intermittent variations of voltages and/or currents are studied for an early recognition of apparition of those incipient faults. The idea of analyzing the evolution of incipient faults is introduced in [3] and [10] and it is based on the identification of parameters that can predict failures of components.…”

Section: State Of the Artmentioning

confidence: 99%

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Analysis of Event Sequences in Power Distribution Systems

Quiroga¹,

Meléndez²,

Herraiz³

et al. 2024

RE&PQJ

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Abstract. In this paper, events registered in power distribution systems are analyzed to recognize sequences of events associated to faults occurred in the network. The events considered in this study are basically voltage sags generated by homopolar faults and registered by power quality monitors installed in the secondary of transformers in distribution substations. The events registered in a measuring point have associated the time of occurrence, and the list of increasingtime ordered events corresponds to a sequence. The aim of this work is to discover the collection of events associated with failures in the network that can be viewed as sequences of events related with the actuation of the protection system. Two algorithms are proposed to recognize these sequences. The methodology is tested with data gathered in different substations which have been manual grouped by the utility 1 .

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Analysis of Event Sequences in Power Distribution Systems

Quiroga¹,

Meléndez²,

Herraiz³

et al. 2024

RE&PQJ

View full text Add to dashboard Cite

show abstract

“…It divides into 2 parts: event-based algorithms and event-oriented algorithms; the typical algorithm is PROWL [79,80].…”

Section: Association Analysismentioning

confidence: 99%

Data Mining for the Internet of Things: Literature Review and Challenges

Chen

Deng

Wan

et al. 2015

International Journal of Distributed Sensor Networks

405

215

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The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

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“…(A, 1), (A, 2), (A, 3), (B, 3), (A, 6), (A, 7), (C, 8), (B, 9), (D, 11), (C, 12), 13), (B, 14), (C, 15).…”

Section: Frequent Episode Discoveryunclassified

“…Intuitively, any frequency should capture the notion of the episode occurring many times in the data and, at the same time, should have an efficient algorithm for computing the same. There are many ways to define frequency, and this has given rise to different algorithms for frequent episode discovery [3,[7][8][9][16][17][18]. In the original framework by Mannila et al [17], frequency was defined as the number of fixed-width sliding windows over the data that contain at least one occurrence of the episode.…”

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confidence: 99%

A unified view of the apriori-based algorithms for frequent episode discovery

2011

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Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

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Efficient mining of frequent episodes from complex sequences

Abstract: Abstract

Cited by 82 publications

References 22 publications

Analysis of Event Sequences in Power Distribution Systems

Analysis of Event Sequences in Power Distribution Systems

Data Mining for the Internet of Things: Literature Review and Challenges

A unified view of the apriori-based algorithms for frequent episode discovery

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