Memory-adaptive high utility sequential pattern mining over data streams
نویسندگان
چکیده
منابع مشابه
Memory-Bounded High Utility Sequential Pattern Mining over Data Streams
Mining high utility sequential patterns (HUSPs) has emerged as an important topic in data mining. However, the existing studies on this topic focus on static data and do not consider streaming data. Streaming data are fast changing, continuously generated and unbounded in amount. Such data can easily exhaust computer resources (e.g., memory) unless proper resource-aware mining is performed. In ...
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High utility sequential pattern mining has emerged as an important topic in data mining. Although several preliminary works have been conducted on this topic, the existing studies mainly focus on mining high utility sequential patterns (HUSPs) in static databases and do not consider the streaming data. Mining HUSPs over data streams is very desirable for many applications. However, addressing t...
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Online high utility itemset mining over data streams has been studied recently. However, the existing methods are not designed for producing topk patterns. Since there could be a large number of high utility patterns, finding only top-k patterns is more attractive than producing all the patterns whose utility is above a threshold. A challenge with finding top-k high utility itemsets over data s...
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Smart home technology is a better choice for the people to care about security, comfort and power saving as well. It is required to develop technologies that recognize the Activities of Daily Living (ADLs) of the residents at home and detect the abnormal behavior in the individual's patterns. Data mining techniques such as Frequent pattern mining (FPM), High Utility Pattern (HUP) Mining were us...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2017
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-016-5617-1