Proposed algorithm for frequent item set generation

Singh, Archana; Agarwal, Jyoti

doi:10.1109/ic3.2014.6897166

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In recent years, massive data mining problem is researched hot by researchers, Patil et al [9] give contribution to improved apriori algorithm by hiding sensitive association rules which are generated by applying improved apriori algorithm on supermarket database. Singh and Agarwal [10] propose a new optimized algorithm and to compare its performance with the existing data mining algorithms. Prasanna and Seetha [11] present a method for generating association rules from large high dimensional data, which can obtain more rapid computing speed and sententious rules.…”

Section: Introductionmentioning

confidence: 99%

Application of Transaction Mining Based on FP-Table Algorithm in Mobile Electricity Market

Gao¹,

Dai²,

Jiao

2015

JSS

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Electricity market trade based on mobile intelligent device will extend the volume of transaction. For the massive and various trading data, transaction mining algorithm is very useful to find the relationship of correlative elements such as trade price and power capacity, and it always occurs between the power users and power generation enterprises. The novel FP-Table algorithm is proposed in this paper to solve the massive transaction mining problem. The FP-Table algorithm integrates the Hash table into FP-Growth algorithm, using two-dimension table saving frequency count of item pair, then mining the frequency items of electricity transactions efficiently. Application of mobile transaction mining is proved to be high efficiency and high value by performance experiment results.

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Section: Introductionmentioning

confidence: 99%

Application of Transaction Mining Based on FP-Table Algorithm in Mobile Electricity Market

Gao¹,

Dai²,

Jiao

2015

JSS

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show abstract

“…Fang and Qizhi [3] proposed an improved Apriori algorithm with better in performance than previous one and performing less scan with generating less number of candidate sets to improve algorithm efficiency. Singh and Agarwal [4] proposed the optimized algorithm i.e. FI generator with fewer numbers of database scans.…”

Section: Related Work On Frequent Pattern Miningmentioning

confidence: 99%