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标题：High Utility Itemset Mining with Selective Item Replication
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作者：V.Narendranath ; P.S.Rajan ; R.Karthikeyan 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：3
DOI：10.15680/ijircce.2015.0303182
出版社：S&S Publications
摘要：Incessant weighted thing sets speak to connections habitually holding in information in which thingsmay weight in an unexpected way. Nonetheless, in a few settings, e.g., when the need is to minimize a certain expensecapacity, finding uncommon information connections is more intriguing than mining regular ones Our technique workson a chart where vertices relate to incessant things and edges compare to successive thing arrangements of size two.Utility based information mining is another examination range inspired by a wide range of utility figures informationmining techniques and focused at consolidating utility contemplations in information mining errands. Utility basedinformation mining is another examination territory keen on a wide range of utility calculates information miningtechniques and focused at consolidating utility contemplations in information mining undertakings. The UMiningcalculation is utilized to discover all high utility itemsets inside the given utility imperative limit. Quick UtilityFrequent Mining, is a more exact and exceptionally late calculation. It takes both the utility and the bolster measureinto thought. This strategy gives the itemsets that are both high utility as well as that may be, visit. Another idea isproposed for creating various types of itemsets specifically High utility and high successive itemsets (HUHF), Highutility and low visit itemsets (HULF), Low utility and high regular itemsets (LUHF) and Low utility and low visititemsets (LULF). These itemsets are produced utilizing the essential structure FP-Growth calculations.
关键词：Clustering; classification; and association rules; data mining