DFP-Growth: an efficient algorithm for mining frequent patterns in dynamic database
Mining frequent patterns in a large database is still an important and relevant topic in data mining. Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. However, the major drawback in FP-Growth is, the FP-Tree must be rebuil...
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| வடிவமà¯: | Conference or Workshop Item |
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2012
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| நிகழà¯à®¨à®¿à®²à¯ˆ அணà¯à®•à®²à¯: | http://dx.doi.org/10.1007/978-3-642-34062-8_7 http://dx.doi.org/10.1007/978-3-642-34062-8_7 |
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| தொகà¯à®ªà¯à®ªà¯: | Mining frequent patterns in a large database is still an important and relevant topic in data mining. Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. However, the major drawback in FP-Growth is, the FP-Tree must be rebuilt all over again once the original database is changed. Therefore, in this paper we introduce an efficient algorithm called Dynamic Frequent Pattern Growth (DFP-Growth) to mine the frequent patterns from dynamic database. Experiments with three UCI datasets show that the DFP-Growth is up to 1.4 times faster than benchmarked FP-Growth, thus verify it efficiencies. |
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