Applying the maximum utility measure in high utility sequential pattern mining

Lan, Guo-Cheng; Hong, Tzung-Pei; Tseng, Vincent S.; Wang, Shyue-Liang

doi:10.1016/j.eswa.2014.02.022

Cited by 107 publications

(68 citation statements)

References 17 publications

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“…In this work, the main concept of proposed approach is extended from the PHUS algorithm [8]. By using this approach, the proposed algorithm does not need to rescan the original sequences for finding high utility sequential patterns from incremental sequences.…”

Section: The Proposed Projection Based Incremental Algorithmmentioning

confidence: 99%

“…A subsequence S is called a high utility sequential pattern (abbreviated as HUS) if asu (S) ≥ λ. For example in Table 1, if λ =55, then the subsequence <ac> is a high utility sequential pattern since asu <ac> ≥λ(55) [8].…”

Section: Definitionmentioning

confidence: 99%

“…For the two utility values, the maximum utility value is 6, and the value of 6 is regarded as the utility of the item a in Seq1. [8] …”

Section: Definitionmentioning

confidence: 99%

“…For example, in Table 1, there are three elements in the fifth sequence Seq5, and their local sequence utility or internal utility values are 3,1 and 1, respectively [8].…”

Section: Definitionmentioning

confidence: 99%

“…I ={ i1,i2,... ,im} is a set of items, which may appear in sequences. For example, the itemset {ab} contains 2 items and is called a 2-itemset [8].…”

Section: Definitionmentioning

confidence: 99%

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Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset

Dave¹,

Shah²

2015

IJCA

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Frequent Pattern mining is modified by Sequential Pattern Mining to consider time regularity which is further enhanced to high utility sequential pattern mining (HUS) by incorporating utility into sequential pattern mining for business value and impact. In the process of mining HUS, when new sequences are added into the existing database the whole procedure of mining HUS starts from the scratch, in spite of mining HUS only from incremental sequences. This results in excess of time as well as efforts. So in this paper an incremental algorithm is proposed to mine HUS from the Incremental Database. Experimental results show that the proposed algorithm executes faster than existing PHUS algorithm resulting in saving of time as well as efforts.

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Section: The Proposed Projection Based Incremental Algorithmmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

“…For the two utility values, the maximum utility value is 6, and the value of 6 is regarded as the utility of the item a in Seq1. [8] …”

Section: Definitionmentioning

confidence: 99%

“…For example, in Table 1, there are three elements in the fifth sequence Seq5, and their local sequence utility or internal utility values are 3,1 and 1, respectively [8].…”

Section: Definitionmentioning

confidence: 99%

“…I ={ i1,i2,... ,im} is a set of items, which may appear in sequences. For example, the itemset {ab} contains 2 items and is called a 2-itemset [8].…”

Section: Definitionmentioning

confidence: 99%

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Efficient Mining of High Utility Sequential Pattern from Incremental Sequential Dataset

Dave¹,

Shah²

2015

IJCA

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Multivariate temporal data analysis ‐ a review

Moskovitch

2021

WIREs Data Min & Knowl

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The information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide meaningful opportunities to understand process in many domains along time, but also challenges. A main challenge is the heterogeneity of the temporal variables due to the different types of data, whether a measurement or an event, and type of samplings: fixed or irregular. Other variables can be also events that may or not have duration. In this review, we discuss the various types of temporal data, and the various relevant analysis methods. Starting with fixed frequency variables, with forecasting and time series methods, and proceeding with sequential data, and sequential patterns mining, and time intervals mining for events having various time duration. Also the use of various deep learning based architectures for temporal data is discussed. The challenge of heterogeneous multivariate temporal data analysis and discuss various options to deal with it, focusing on an increasingly used option of transforming the data into symbolic time intervals through temporal abstraction and the use of time intervals related patterns discovery for temporal knowledge discovery, clustering, classification prediction, and more. Finally, we discuss the overview of the field, and areas in which more studies and contributions are needed. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mininganalytics, temporal data mining, time series | INTRODUCTIONWith the introduction of the internet, an exponential growth in text based data, which required new methods to analyze text, retrieve, understand, classify, and more. However, with the recent growing adoption of the Internet of Things, in which devices in various domains become connected to the internet network and produce multitude of logged longitudinal data, we are facing a similar increase in multivariate temporal data that is generated by various devices and sensors. Such data can be logged from smart watches, cars, smart refrigerators, smart phones, televisions, sensors in the space of rooms for various purposes, and many more, which can be useful in various domains, and for different tasks. This trend is expected to increase significantly and requires new methods and more scalability.An extension of temporal data analytics, which are out of the scope of this article, is spatio-temporal data analysis (Gong

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