TDUP: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating

Yao, Li; Zhang, Zhiheng; Chen, Wenbin; Min, Fan

doi:10.1007/s13042-015-0337-6

Cited by 42 publications

(17 citation statements)

References 23 publications

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“…In addition to these techniques, a number of techniques have been proposed for mining rules in a dynamic database. These are pattern based [15,16], tree based [17], three-way decision based [18,19], probability-based [20,21]. These techniques take into account the faster processing, e.g., efficiency of mining process by reducing the scan of dataset instead of processing the merged dataset that includes the original dataset and the incremental part of the dataset.…”

Section: Background and Related Workmentioning

confidence: 99%

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

2019

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IntroductionNowadays, smartphones are considered as essential devices in our daily life. Due to the recent advanced features in smartphones and the popularity of context-awareness in mobile technologies, individual's behavioral activities with their phones, such as phone call activities, mobile applications usage, mobile notification responses, social networking, and corresponding contextual information are recorded through the device logs. An individual smartphone's ability to store user's such diverse activities and associated contexts with their phones enables the study on data-driven smartphone usage behavior modeling and prediction [1]. In this paper, we aim to mine a set of personalized recent behavioral patterns, i.e., recency, based rules utilizing individual's contextual phone log data, for the purpose of building an effective context-aware personalized usage behavior prediction model. To illustrate the efficacy of our proposed approach, in this paper, we Abstract Due to the advanced features in recent smartphones and context-awareness in mobile technologies, users' diverse behavioral activities with their phones and associated contexts are recorded through the device logs. Behavioral patterns of smartphone users may vary greatly between individuals in different contexts-for example, temporal, spatial, or social contexts. However, an individual's phone usage behavior may not be static in the real-world changing over time. The volatility of usage behavior will also vary from user-to-user. Thus, an individual's recent behavioral patterns and corresponding machine learning rules are more likely to be interesting and significant than older ones for modeling and predicting their phone usage behavior. Based on this concept of recency, in this paper, we present an approach for mining recency-based personalized behavior, and name it "RecencyMiner" for short, utilizing individual's contextual smartphone data, in order to build a context-aware personalized behavior prediction model. The effectiveness of RecencyMiner is examined by considering individual smartphone user's real-life contextual datasets. The experimental results show that our proposed recency-based approach better predicts individual's phone usage behavior than existing baseline models, by minimizing the error rate in various context-aware test cases.

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Section: Background and Related Workmentioning

confidence: 99%

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

2019

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“…This was named VSIFP-Growth (Improved FP-Growth) and used together with parallel computing techniques with the purpose of developing the PVSIFP-Growth algorithm for frequent itemsets generation. Li et al [16] developed T DU P , a three-way decision update pattern approach together with a synchronization mechanism in order to reduce the number of scans of the initial data set.…”

Section: 3mentioning

confidence: 99%

Incremental Relational Association Rule Mining of Educational Data Sets

Crivei

2018

Studia UBB Informatica

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Educational Data Mining is an attractive research field in which the underlying idea is that of bringing the data mining perspective into educational environments. The main focus is to better understand the educational related phenomena by extracting, through data mining techniques, meaningful hidden patterns from educational data sets. Incremental Relational Association Rule Mining (IRARM) has been introduced as an effective online data mining method for dynamically mining interesting relational association rules (RARs) in a dynamic data set which is extended with new data instances. The study conducted in this paper is aimed to emphasize the effectiveness of both RAR and IRARM mining methods in educational data mining settings. Experiments performed on various academic data sets highlight the potential of using relational association rules for uncovering relevant knowledge from educational related data.

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“…Li et al [13] have proposed a three-way decision update pattern approach (T DU P ) combined with a synchronization mechanism for efficiently updating and maintaining the frequent itemsets. It is based on using an additional support-based measure, so as to classify all possible itemsets into positive, boundary, and negative regions.…”

Section: 1mentioning

confidence: 99%

An Adaptive Gradual Relational Association Rules Mining Approach

Miholca

2018

Studia UBB Informatica

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This paper focuses on adaptive Gradual Relational Association Rules mining. Gradual Relational Association Rules capture gradual generic relations among data features. We propose AGRARM , an algorithm for mining the interesting Gradual Relational Association Rules characterizing a data set that has been extended with a number of new attributes, through adapting the set of interesting rules mined before extension, so as to preserve the completeness. We aim, through AGRARM , to make the mining process more efficient than resuming the mining algorithm on the enlarged data. We have experimentally evaluated AGRARM versus mining from scratch on three publicly available data sets. The obtained reduction in mining time highlights AGRARM 's efficiency, thus confirming the potential of our proposal.

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TDUP: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating

Cited by 42 publications

References 23 publications

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

Incremental Relational Association Rule Mining of Educational Data Sets

An Adaptive Gradual Relational Association Rules Mining Approach

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