Performance Analysis of Frequent Pattern Mining with Multiple Minimum Supports
نویسندگان
چکیده
منابع مشابه
IWFPM: Interested Weighted Frequent Pattern Mining with Multiple Supports
Association rules mining has been under great attention and considered as one of momentous area in data mining. Classical association rules mining approaches make implicit assumption that items’ importance is the same and set a single support for all items. This paper presents an efficient approach for mining users’ interest weighted frequent patterns from a transactional database. Our paradigm...
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Mining frequent patterns in transactional databases is an important part of the association rule mining. Frequent pattern mining algorithms with single minsup leads to rare item problem. Instead of setting single minsup for all items, we have used multiple minimum supports to discover frequent patterns. In this research, we have used multiple item support tree (MIS-Tree for short) to mine frequ...
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Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items or itemsets. But in real applications, different items may have different criteria to judge its importance. The support requirements should then vary with different items. In t...
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Negative Frequent Item Sets (NFIS) like (a1a2¬a3a4) have played important roles in real applications because many valued negative association rules can be found from them. Very few methods are available for mining NFIS and most of them only use single minimum support, which implicitly assumes that all items in the database are of the same nature or of similar frequencies in the database. This i...
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ژورنال
عنوان ژورنال: Journal of Korean Society for Internet Information
سال: 2013
ISSN: 1598-0170
DOI: 10.7472/jksii.2013.14.6.01