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  • Open Access


    High Utility Periodic Frequent Pattern Mining in Multiple Sequences

    Chien-Ming Chen1, Zhenzhou Zhang1, Jimmy Ming-Tai Wu1, Kuruva Lakshmanna2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463

    Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. More >

  • Open Access


    Hybrid Recommender System Using Systolic Tree for Pattern Mining

    S. Rajalakshmi1,*, K. R. Santha2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1251-1262, 2023, DOI:10.32604/csse.2023.024036

    Abstract A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest… More >

  • Open Access


    A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

    Ham Nguyen1, Tuong Le2,3,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3571-3583, 2022, DOI:10.32604/cmc.2022.024765

    Abstract The task of mining erasable patterns (EPs) is a data mining problem that can help factory managers come up with the best product plans for the future. This problem has been studied by many scientists in recent times, and many approaches for mining EPs have been proposed. Erasable closed patterns (ECPs) are an abbreviated representation of EPs and can be considered condensed representations of EPs without information loss. Current methods of mining ECPs identify huge numbers of such patterns, whereas intelligent systems only need a small number. A ranking process therefore needs to be applied… More >

  • Open Access


    Mining Software Repository for Cleaning Bugs Using Data Mining Technique

    Nasir Mahmood1, Yaser Hafeez1, Khalid Iqbal2, Shariq Hussain3, Muhammad Aqib1, Muhammad Jamal4, Oh-Young Song5,*

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 873-893, 2021, DOI:10.32604/cmc.2021.016614

    Abstract Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules… More >

  • Open Access


    An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

    Yujie Zhang1, Genlin Ji1,*, Bin Zhao1, Bo Sheng2

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 885-901, 2019, DOI:10.32604/cmc.2019.05612

    Abstract The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment, which leverages new applications and services. Since the trajectory streams is rapidly evolving, continuously created and cannot be stored indefinitely in memory, the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams. This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models. By processing the trajectory data in current window, the mining algorithm can capture More >

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