Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Mai, Thang; Nguyen, Loan T. T.; Vo, Bay; Yun, Unil; Hong, Tzung-Pei

doi:10.3390/s20041078

Cited by 28 publications

(12 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results have shown that the introduced structure has enhanced the runtime by orders of magnitude and significantly reduced memory requirements. Vo et al inherited a lattice-based approach [29], [30] to mine high utility association rules [31] and non-redundant high utility association rules [32].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Nguyen

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Analyzing customer transactions to discover high-utility itemsets is a popular task, which consists of finding the sets of items that are purchased together and yield a high profit. However, many studies assume that transactional data is static while in real-life, it changes over time. For example, the unit profits of items may vary from one week to another because sale prices and production costs may change. Many algorithms for mining high-utility itemsets (HUI) ignore this important property and thus are inapplicable or generate inaccurate results on real data. To address this issue, this paper proposes a novel algorithm named Multi-Core HUI Miner (MCH-Miner). It adapts techniques introduced in the iMEFIM algorithm to run on a parallel multi-core architecture to efficiently mine HUIs in dynamic transaction databases. An empirical evaluation shows that in most cases, MCH-Miner is significantly faster than iMEFIM, and that the cost of database scans is reduced. INDEX TERMS Data mining, high utility itemset, dynamic profit, parallel, multithread.

show abstract

Section: Related Workmentioning

confidence: 99%

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Nguyen

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The problem of association rule mining was first identified in [1], which received widespread attention. Several wellknown algorithms for association rule mining have been developed, such as Apriori [2], Eclat [39], FP-Growth [10], and NR-HARs [22] algorithms. Some well-konwn studies [38] [36] have attempted to elaborate efficient frequent itemset/pattern mining algorithms for transactional datasets or the data from the Internet of Things.…”

Section: Related Workmentioning

confidence: 99%

DPARM: Differentially Private Association Rules Mining

et al. 2020

View full text Add to dashboard Cite

Association analysis is critical in data analysis performed to find all co-occurrence relationships (i.e., frequent itemsets or confident association rules) from the transactional dataset. An association rule can improve the ability of users to discover patterns and develop corresponding strategies. The data analysis process can be summarized as a set of queries, where each query is a real-valued function of the dataset. However, unless restrictions and protections are implemented, accessing the dataset to answer the queries may lead to the disclosure of the private information of individuals. In this paper, we propose an original differentially private association rules mining (DPARM) algorithm, which uses multiple support thresholds to reduce the number of candidate itemsets while reflecting the real nature of the items and uses random truncation and uniform partition to reduce the dimensionality of the dataset. Both of these elaborated approaches can aid in reducing the sensitivity of the queries, and this dramatically reduces the scale of the required noise and improves the utility of the mining results. We significantly stabilize the noise scale by adaptively allocating the privacy levels and bound the overall privacy loss. Through a series of experiments, we prove that our DPARM algorithm outperforms the literature in the accuracy of data mining while satisfying differential privacy. To the best of our knowledge, our work is the first DPARM algorithm to adopt multiple support thresholds while using a set of elaborated approaches to bound the overall privacy loss of the mining process. INDEX TERMS Privacy-preserving data analysis; differential privacy; association analysis; association rules mining; frequent itemset mining.

show abstract

“…Commonly used mobility prediction models include association rule mining-based models [ 12 , 13 , 14 ], Markov chain-based models [ 15 , 16 , 17 , 18 , 19 ], and neural network-based models [ 20 , 21 ]. Association rule mining is based on the regularity and periodicity of the user itineraries, and location prediction is performed by mining the key stops and frequent routes of mobile users [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

Yan

Chan

et al. 2021

Sensors

View full text Add to dashboard Cite

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

show abstract

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Cited by 28 publications

References 39 publications

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

DPARM: Differentially Private Association Rules Mining

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

Contact Info

Product

Resources

About