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Author: search.filters.author.Fournier-Viger, Philippe

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Now showing 1 - 10 of 17
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    Mining high-utility itemsets in dynamic profit databases
    (2019-07-01)
    Nguyen, Loan T. T.
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    Phuc Nguyen
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    Nguyen, Trinh D. D.
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    Vo, Bay
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    Fournier-Viger, Philippe
    High-Utility Itemset (HUI) mining is an important data-mining task which has gained popularity in recent years due to its applications in numerous fields. HUI mining aims at discovering itemsets that have high utility (e.g., yield a high profit) in transactional databases. Although several algorithms have been designed to enumerate all Mils, an important issue is that they assume that the utilities (e.g., unit profits) of items are static. But this simplifying assumption does not hold in real-life situations. For example, the unit profits of items often vary over time in a retail store due to fluctuating supply costs and promotions. Ignoring this important characteristic of real-life transactional databases makes current HUI-mining algorithms inapplicable in many real-world applications. To address this critical limitation of current HUI-mining techniques, this paper studies the novel problem of mining HUls in databases having dynamic unit profits. To accurately assess the utility of any itemset in this context, a redefined utility measure is introduced. Furthermore, a novel algorithm named MEFIM (Modified EFficient high-utility Itemset Mining), which relies on a novel compact database format to discover the desired itemsets efficiently, is designed. An improved version of the MEFIM algorithm, named iMEFIM, is also introduced. This algorithm employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on dynamic profit databases in terms of runtime, memory usage, and scalability. (C) 2019 Elsevier B.V. All rights reserved.
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    Analyzing Students' Attention in Class Using Wearable Devices
    (2017-01-01)
    Zhang, Xin
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    Wu, Cheng-Wei
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    Fournier-Viger, Philippe
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    Van, Lan-Da
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    Tseng, Yu-Chee
    Detecting students' attention in class provides key information to teachers to capture and retain students' attention. Traditionally, such information is collected manually by human observers. Wearable devices, which have received a lot of attention recently, are rarely discussed in this field. In view of this, we propose a multimodal system which integrates a head-motion module, a pen-motion module, and a visual-focus module to accurately analyze students' attention levels in class. These modules collect information via cameras, accelerometers, and gyroscopes integrated in wearable devices to recognize students' behaviors. From these behaviors, attention levels are inferred for various time periods using a rule-based approach and a data-driven approach. The former infers a student's attention states using user-defined rules, while the latter relies on hidden relationships in the data. Extensive experimental results show that the proposed system has excellent performance and high accuracy. To the best of our knowledge, this is the first study on attention level inference in class using wearable devices. The outcome of this research has the potential of greatly increasing teaching and learning efficiency in class.
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    Efficiently mining uncertain high-utility itemsets
    (2017-06-01)
    Lin, Jerry Chun-Wei
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    Gan, Wensheng
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    Fournier-Viger, Philippe
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    Hong, Tzung-Pei
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    Tseng, Vincent S.
    Data mining consists of deriving implicit, potentially meaningful and useful knowledge from databases such as information about the most profitable items. High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms can only handle precise data, although big data collected in real-life applications using experimental measurements or noisy sensors is often uncertain. In this paper, an efficient algorithm, named Mining Uncertain High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) in uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mines PHUIs without generating candidates, and can avoid constructing PU-lists for numerous unpromising itemsets by applying several efficient pruning strategies, which greatly improve its performance. Extensive experiments conducted on both real-life and synthetic datasets show that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, and that the proposed MUHUI algorithm scales well when mining PHUIs in large-scale uncertain datasets.
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    EFIM: a fast and memory efficient algorithm for high-utility itemset mining
    (2017-05-01)
    Zida, Souleymane
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    Fournier-Viger, Philippe
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    Lin, Jerry Chun-Wei
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    Wu, Cheng-Wei
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    Tseng, Vincent S.
    In recent years, high-utility itemset mining has emerged as an important data mining task. However, it remains computationally expensive both in terms of runtime and memory consumption. It is thus an important challenge to design more efficient algorithms for this task. In this paper, we address this issue by proposing a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discover high-utility itemsets. EFIM relies on two new upper bounds named revised sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques named High-utility Database Projection and High-utility Transaction Merging (HTM), also performed in linear time. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster than the state-of-art algorithms HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+ on dense datasets and performs quite well on sparse datasets. Moreover, a key advantage of EFIM is its low memory consumption.
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    Efficient algorithms for mining high-utility itemsets in uncertain databases
    (2016-03-15)
    Lin, Jerry Chun-Wei
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    Gan, Wensheng
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    Fournier-Viger, Philippe
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    Hong, Tzung-Pei
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    Tseng, Vincent S.
    High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high utility itemsets (HUIs) assume that the information stored in databases is precise, i.e., that there is no uncertainty. But in many real-life applications, an item or itemset is not only present or absent in transactions but is also associated with an existence probability. This is especially the case for data collected experimentally or using noisy sensors. In the past, many algorithms were respectively proposed to effectively mine frequent itemsets in uncertain databases. But mining HUIs in an uncertain database has not yet been proposed, although uncertainty is commonly seen in real-world applications. In this paper, a novel framework, named potential high-utility itemset mining (PHUIM) in uncertain databases, is proposed to efficiently discover not only the itemsets with high utilities but also the itemsets with high existence probabilities in an uncertain database based on the tuple uncertainty model. The PHUI-UP algorithm (potential high-utility itemsets upper-bound-based mining algorithm) is first presented to mine potential high-utility itemsets (PHUIs) using a level-wise search. Since PHUI-UP adopts a generate-and test approach to mine PHUIs, it suffers from the problem of repeatedly scanning the database. To address this issue, a second algorithm named PHUI-List (potential high-utility itemsets PU-list-based mining algorithm) is also proposed. This latter directly mines PHUIs without generating candidates, thanks to a novel probability-utility-list (PU-list) structure, thus greatly improving the scalability of PHUI mining. Substantial experiments were conducted on both real-life and synthetic datasets to assess the performance of the two designed algorithms in terms of runtime, number of patterns, memory consumption, and scalability. (C) 2015 Elsevier B.V. All rights reserved.
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    Weighted frequent itemset mining over uncertain databases
    (2016-01)
    Lin, Jerry Chun-Wei
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    Gan, Wensheng
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    Fournier-Viger, Philippe
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    Hong, Tzung-Pei
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    Tseng, Vincent S.
    Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete. To address these issues and mine more useful and meaningful knowledge, the problems of weighted and uncertain itemset mining have been respectively proposed, where a user may respectively assign weights to items to specify their relative importance, and specify existential probabilities to represent uncertainty in transactions. However, no work has addressed both of these issues at the same time. In this paper, we address this important research problem by designing a new type of patterns named high expected weighted itemset (HEWI) and the HEWI-Uapriori algorithm to efficiently discover HEWIs. The HEWI-Uapriori finds HEWIs using an Apriori-like two-phase approach. The algorithm introduces a property named high upper-bound expected weighted downward closure (HUBEWDC) to early prune the search space and unpromising itemsets. Substantial experiments on real-life and synthetic datasets are conducted to evaluate the performance of the proposed algorithm in terms of runtime, memory consumption, and number of patterns found. Results show that the proposed algorithm has excellent performance and scalability compared with traditional methods for weighted-itemset mining and uncertain itemset mining.
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    Fast algorithms for mining high-utility itemsets with various discount strategies
    (2016-04)
    Lin, Jerry Chun-Wei
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    Gan, Wensheng
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    Fournier-Viger, Philippe
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    Hong, Tzung-Pei
    ;
    Tseng, Vincent S.
    In recent years, mining high-utility itemsets (HUIs) has emerged as a key topic in data mining. It consists of discovering sets of items generating a high profit in a transactional database by considering both purchase quantities and unit profits of items. Many algorithms have been proposed for this task. However, most of them assume the unrealistic assumption that unit profits of items remain unchanged over time. But in real-life, the profit of an item or itemset varies as a function of cost prices, sales prices and sale strategies. Recently, a three-phase algorithm has been proposed to mine HUIs, while considering that each item may have different discount strategies. However, the complete set of HUIs cannot be retrieved based on the traditional TWU model with its defined discount strategies. Moreover, it suffers from the well-known drawbacks of Apriori-based algorithms such as maintaining a huge amount of candidates in memory and repeatedly performing time-consuming database scans. In this paper, a HUI-DTP algorithm for mining HUIs when considering discount strategies of items is introduced. The HUI-DTP is designed as a two-phase algorithm to mine the complete set of HUIs based on a novel downward closure property and a vertical TID-list structure. Furthermore, the HUI-DMiner is an algorithm relying on a compact data structure (Positive-and-Negative Utility-list, PNU-list) and properties of two new pruning strategies to efficiently discover HUIs without candidate generation, while considerably reducing the size of the search space. Moreover, a strategy named Estimated Utility Co-occurrence Strategy which stores the relationships between 2-itemsets is also applied in the improved HUI-DEMiner algorithm to speed up computation. An extensive experimental study carried on several real-life datasets shows that the proposed algorithms outperform the previous.best algorithm in terms of runtime, memory consumption and scalability. (C) 2016 Elsevier Ltd. All rights reserved.
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    Efficient Mining of Uncertain Data for High-Utility Itemsets
    (2016)
    Lin, Jerry Chun-Wei
    ;
    Gan, Wensheng
    ;
    Fournier-Viger, Philippe
    ;
    Hong, Tzung-Pei
    ;
    Tseng, Vincent S.
    High-utility itemset mining (HUIM) is emerging as an important research topic in data mining. Most algorithms for HUIM can only handle precise data, however, uncertainty that are embedded in big data which collected from experimental measurements or noisy sensors in real-life applications. In this paper, an efficient algorithm, namely Mining Uncertain data for High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) from uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mine PHUIs without candidate generation and can reduce the construction of PU-lists for numerous unpromising itemsets by using several efficient pruning strategies, thus greatly improving the mining performance. Extensive experiments both on real-life and synthetic datasets proved that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, especially, the MUHUI algorithm scales well on large-scale uncertain datasets for mining PHUIs.
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    EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets
    (2016)
    Fournier-Viger, Philippe
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    Zida, Souleymane
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    Lin, Jerry Chun-Wei
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    Wu, Cheng-Wei
    ;
    Tseng, Vincent S.
    Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To provide a concise and lossless representation of results to the user, the concept of closed high-utility itemsets was proposed. However, mining closed high-utility itemsets is computationally expensive. To address this issue, we present a novel algorithm for discovering closed high-utility itemsets, named EFIM-Closed. This algorithm includes novel pruning strategies named closure jumping, forward closure checking and backward closure checking to prune non-closed high-utility itemsets. Furthermore, it also introduces novel utility upper-bounds and a transaction merging mechanism. Experimental results shows that EFIM-Closed can be more than an order of magnitude faster and consumes more than an order of magnitude less memory than the previous state-of-art CHUD algorithm.
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    Mining Minimal High-Utility Itemsets
    (2016)
    Fournier-Viger, Philippe
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    Lin, Jerry Chun-Wei
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    Wu, Cheng-Wei
    ;
    Tseng, Vincent S.
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    Faghihi, Usef
    Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high-utility itemsets have been proposed such as closed HUIs, maximal HUIs and generators of HUIs. In this paper, we explore a novel representation called the minimal high utility itemsets (MinHUIs), defined as the smallest sets of items that generate a high profit, study its properties, and design an efficient algorithm named MinFHM to discover it. An extensive experimental study with real-life datasets shows that mining MinHUIs can be much faster than mining other concise representations or all HUIs, and that it can greatly reduce the size of the result set presented to the user.