An incremental updating algorithm for mining association rules

Xu, Baowen; Tong, Yanhong; Wu, Fangjun; Chen, Zhenqiang

doi:10.1007/s11767-002-0073-4

Cited by 16 publications

(7 citation statements)

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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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“…Several incremental mining techniques have been proposed for mining rules in a dynamic dataset. For instance, frequent pattern based [2,3,15,17], three-way decision based [6,18], and probability-based [1,16] techniques efficiently maintain association rules of a dynamic dataset. These techniques produce updated rules by taking into account the incremental dataset and the knowledge of existing rules produced from initial dataset.…”

Section: Incremental Miningmentioning

confidence: 99%

Understanding recency-based behavior model for individual mobile phone users

Sarker

Kabir

Colman

et al. 2017

Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 AC

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“…Zhang et al [20] extended the traditional SVM, Robust SVM and one-class SVM to be of online incremental forms. Baowen et al [21] proposed an incremental algorithm for mining association rules. The algorithm considers not only adding new data into the knowledge base but also reducing old data from the knowledge base.…”

Section: Related Workmentioning

confidence: 99%

An Intrusion Detection and Prevention System based on Automatic Learning of Traffic Anomalies

Nasr¹,

Ezz²,

Abdulmaged³

2016

IJCNIS

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The ever changing network traffic reveals new attack types, which represent a security threat that poses a serious risk for enterprise resources. Therefore, the security administrators are in a real need to employ efficient Intrusion Detection and Prevention Systems, IDPS. Such systems might be capable to learn from the network behavior. In this paper, we present an incremental Learnable Model for Anomaly Detection and Prevention of Zero-day attacks, LMAD/PZ. To facilitate the ability of learning from observations that can provide a reliable model for automatic prevention, a comparison has been carried out between supervised and unsupervised learning techniques.Thus, in LMAD/PZ, the intrusion detection step is integrated with an intrusion prevention plan. To ensure that the prevention plan is dependable and automatic, it must be backed and sustained with robust and accurate detection process. Therefore, two incremental data mining techniques are deeply investigated and implemented on NSL-KDD'99 intrusion dataset. The first technique is the Algorithm Quasi-optimal (AQ), which is a supervised Attributional Rules Learner, ARL, while the second is the Cobweb; an unsupervised hierarchical conceptual clustering algorithm. These algorithms categorize the network connections as either normal or anomalous. The performance of AQ is compared to Cobweb, and the best performance result is integrated with the prevention plan, to afford a fully automated system. The experimental results showed that, the model automatically adapts its knowledge base from continuous network streams, in addition to offering the advantage of detecting novel and zero day attacks. Many experiments have verified that AQ performance outperforms the Cobweb clustering, in terms of accuracy, detection rate and false alarm rate.

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An incremental updating algorithm for mining association rules

Cited by 16 publications

References 1 publication

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

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

Understanding recency-based behavior model for individual mobile phone users

An Intrusion Detection and Prevention System based on Automatic Learning of Traffic Anomalies

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