A Method for Frequent Itemsets Mining from Data Stream
نویسندگان
چکیده
منابع مشابه
Mining maximal frequent itemsets from data streams
Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store meta-patterns obtained by scanning the stream data; and (2) re-mining the meta-patterns to finalize and output frequent patterns. The defectiveness of such a two-phase framew...
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Frequent itemset mining plays an important part in college library data analysis. Because there are a lot of redundant data in library database, the mining process may generate intra-property frequent itemsets, and this hinders its efficiency significantly. To address this issue, we propose an improved FP-Growth algorithm we call RFP-Growth to avoid generating intra-property frequent itemsets, ...
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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For...
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ژورنال
عنوان ژورنال: The KIPS Transactions:PartD
سال: 2012
ISSN: 1598-2866
DOI: 10.3745/kipstd.2012.19d.2.139