MS-FP-Growth: A Multi-support Version of FP-Growth Algorithm

Taktak, Wiem; Slimani, Yahya

doi:10.14257/ijhit.2014.7.3.16

Cited by 5 publications

(2 citation statements)

References 8 publications

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Section: Related Workmentioning

confidence: 99%

“…Taktak and Slimani [2] have proposed a multi-support variation in the versions of FP Growth by handling two cases of increasing and decreasing the minsup values. They have generated rare rules carrying out their work [2]. Hipp et al [3] have described the fundamentals of the association rule mining by deriving a general framework for the same.…”

Section: Related Workmentioning

confidence: 99%

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FP-Growth Implementation Using Tries for Association Rule Mining

Goel

2017

Advances in Intelligent Systems and Computing

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Abstract. With the advent of technology the past few years have seen the rise in the field of data mining. Data mining generally considered as the process of extracting the useful information by finding the hidden and the non-trivial information out of large chunks of dataset. Now here comes the role of association rule mining which forms a crucial component of data mining. Many recent applications like market basket analysis, text mining etc. are done using this approach. In this paper we have discussed the novel approach to implement the FP Growth method using the trie data structure. Tries provide a special feature of merging the shared sets of data with the number of occurrences that were already registered as count. So this paper widely gives an idea about how the interesting patterns are generated from the large databases using association rule mining methodologies.

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Section: Related Workmentioning

confidence: 99%