Multi-Source Data Stream Online Frequent Episode Mining
نویسندگان
چکیده
منابع مشابه
Frequent Sets Mining in Data Stream Environments
In recent years, data streams have emerged as a new data type that has attracted much attention from the data mining community. They arise naturally in a number of applications (Brian et al., 2002), including financial service (stock ticker, financial monitoring), sensor networks (earth sensing satellites, astronomic observations), web tracking and personalization (webclick streams). These stre...
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During the past decade, stream data mining has been attracting widespread attentions of the experts and the researchers all over the world and a large number of interesting research results have been achieved. Among them, frequent itemset mining is one of main research branches of stream data mining with a fundamental and significant position. In order to further advance and develop the researc...
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Knowledge embedded in a data stream is likely to be changed as time goes by. Identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data element...
متن کاملFrequent Patterns mining in time-sensitive Data Stream
Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns’ mining has much more information to track and much greater complexity to manage. Infrequent items can become frequent later on and hence cann...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2997337