HUOPM: High-Utility Occupancy Pattern Mining

Gan, Wensheng; Lin, Jerry Chun‐Wei; Fournier-Viger, Philippe; Chao, Han-Chieh; Yu, Philip S.

doi:10.1109/tcyb.2019.2896267

Cited by 134 publications

(79 citation statements)

References 44 publications

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“…Gan et al [28] extended the occupancy measure to assess the utility of patterns in transaction databases. The authors then proposed the high-utility occupancy pattern mining (HUOPM) algorithm.…”

Section: High-utility Itemset Miningmentioning

confidence: 99%

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

Mai

Nguyen

et al. 2020

Sensors

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In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.

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Section: High-utility Itemset Miningmentioning

confidence: 99%

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

Mai

Nguyen

et al. 2020

Sensors

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“…Lin et al first proposed an utility mining model to extract the high-utility patterns from uncertain databases [20]. Different from the above utility measures of HUPs, another utility measure for utility-driven pattern mining namely utility occupancy is introduced recently [39]. And a comprehensive survey of utility mining has been provided by Gan et al [13].…”

Section: Utility-based Pattern Mining With Effectiveness Issuesmentioning

confidence: 99%

Utility-Driven Data Analytics on Uncertain Data

Gan

Lin

Chao

et al. 2020

IEEE Systems Journal

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Modern Internet of Things (IoT) applications generate massive amounts of data, much of it in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different levels of importance (e.g., unit utility of items, interestingness, cost, risk, or weight). Many existing approaches in data mining and analytics have limitations such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented, to extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for risk prediction, manufacturing management, decision-making, among others. By using the developed vertical Probability-Utility list with the Positive-and-Negative utilities (PU ± -list) structure, as well as several effective pruning strategies. Experiments showed that the developed HUPNU approach performed great in mining the qualified patterns efficiently and effectively.

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“…For mining rules, the major idea is motivated from data mining theory [43][44][45][46][47][48] that, an association rule A = > B indicates that transactions in the database containing an item set A tend to contain an item set B, and the related confidence exceeds the threshold. From another viewpoint, a rule can be viewed as a high confident rule.…”

Section: Course Suggestionmentioning

confidence: 99%

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

Su¹,

Liao²,

Chen³

2019

Applied Sciences

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For a student, determining how to choose from a set of courses is an important issue prior to learning. An appropriate learning guide can direct students toward an area of interest. The learning results produced by the student in this case are superior due to their strong interest in the subject matter. Although a number of methods have been proposed to address this issue, the effectiveness remains unsatisfactory. To this end, we created an effective system, called the personality-driven course decision assistant, to help students determine the courses they should select by mining and filtering learners’ personality patterns. For learner pattern mining, the relationships between the students’ learning results and the referred personalities are discovered to provide the learners with valuable information before learning. For filtering learner personality patterns, students with similar personality patterns are filtered to predict the potential learning results. Through the actual system, a number of subjective and objective evaluations were conducted, and the evaluation results reveal that the proposed system is highly effective and reliable.

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HUOPM: High-Utility Occupancy Pattern Mining

Cited by 134 publications

References 44 publications

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

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

Utility-Driven Data Analytics on Uncertain Data

An Intelligent Course Decision Assistant by Mining and Filtering Learners’ Personality Patterns

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