文章基本信息

标题：OBHUSPC - OPTIMAL BIG HIGH UTILITY SEQUENTIAL PATTERNS MINING WITH CUCKOO OPTIMIZATION USING HADOOP MAP REDUCE FRAMEWORK
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作者：V. MALSORU ; A. R. NASEER ; G. NARSIMHA 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2020
卷号：98
期号：21
页码：3436-3450
出版社：Journal of Theoretical and Applied
摘要：The unprecedented explosion of information and technology areas in recent years has resulted in the generation of massive amount of data and extracting necessary and sensitive information from these huge amount of data has become a critical and challenging task. In this direction, several data mining approaches are proposed which lay the foundation for faster and efficient knowledge discovery. Big data High Utility Sequential Pattern (HUSP) Mining has turned out to be an important and essential data mining area where several investigations are carried out for effectual information retrieval. In this work, an effectual procedure for sequential pattern mining of Big High Utility Sequential Patterns along with the Binary Cuckoo Search Optimization (OBHUSPC) is proposed. Here Big High Utility Sequential Patterns mining approach is deployed to find the High Utility Sequential Patterns from big data and the Binary Cuckoo Search Optimization (BCSO) technique is used additionally to determine efficiently the finest high utility sequences. The proposed parallel method is implemented in Hadoop disseminated atmosphere to resolve the scalability difficulty and a transformed database is implemented to diminish the scanning time. The performance of the approach used in this work is evaluated using JAVA platform based Hadoop and Map-reduce framework with various big datasets.
关键词：Map-Reduce Framework;Data Mining;Big High Utility Sequential Patterns;Binary Cuckoo Search Optimization;Sequential Pattern Mining