A primer to frequent itemset mining for bioinformatics
نویسندگان
چکیده
منابع مشابه
A primer to frequent itemset mining for bioinformatics
Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of...
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During the last ten years, many algorithms have been proposed to mine frequent itemsets. In order to fairly evaluate their behavior, the IEEE/ICDM Workshop on Frequent Itemset Mining Implementations (FIMI’03) has been recently organized. According to its analysis, kDCI++ is a state-of-the-art algorithm. However, it can be observed from the FIMI’03 experiments that its efficient behavior does no...
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Data mining is a rapidly expanding field being applied in many disciplines, ranging from remote sensing to geographical information systems, computer cartography, environmental assessment and planning. Rule mining is a powerful technique used to discover interesting associations between attributes contained in a database (Han et al., 2006). Association rules can have one or several output attri...
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Cloud computing uses the paradigm of data mining-as-a-service. A company/store lacking in mining expertise can outsource its mining needs to a service provider (server). The item-set of the outsourced database are the private property of the data owner. To protect this corporate privacy, the data owner encrypts the data and sends to the server. Based on the mining queries sent from client side,...
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ژورنال
عنوان ژورنال: Briefings in Bioinformatics
سال: 2013
ISSN: 1467-5463,1477-4054
DOI: 10.1093/bib/bbt074