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- W2547125231 abstract "Extraction of interesting negative association rules from large data sets is measured as a key feature of data mining. Many researchers discovered numerous algorithms and methods to find out negative and positive association rules. From the existing approaches, the frequent pattern growth (FP-Growth) approach is well-organized and capable method for finding the item sets which are frequent, without the generation of candidate item sets. The drawback of FP-Growth is it discovers a huge amount of conditional FP-Tree. We propose a novel, improved FP-Tree for extracting negative association rules without generating conditional FP-Tree." @default.
- W2547125231 created "2016-11-11" @default.
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- W2547125231 date "2014-12-01" @default.
- W2547125231 modified "2023-09-27" @default.
- W2547125231 title "Mining of negative association rules using improved frequent pattern tree" @default.
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- W2547125231 doi "https://doi.org/10.1109/iccct2.2014.7066748" @default.
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