A Novel Incremental Data Mining Algorithm Based on FP-growth for Big Data

Chang, Hong-Yi; Lin, Jia-Chi; Cheng, Mei-Li; Huang, Shih-Chang

doi:10.1109/nana.2016.77

Cited by 26 publications

(12 citation statements)

References 8 publications

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“…Association rule mining is an important data analysis method and data mining technology [9]. Although Agrawal et al proposed the Apriori algorithm, the algorithm uses iterative process for the data subset and uses the candidate itemsets produced earlier to generate frequent itemsets later, which results in low efficiency of the algorithm and being difficult to be used in the mining of massive data [10,11].…”

Section: Related Workmentioning

confidence: 99%

An improvement of FP-Growth association rule mining algorithm based on adjacency table

et al. 2018

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FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.

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Section: Related Workmentioning

confidence: 99%

An improvement of FP-Growth association rule mining algorithm based on adjacency table

et al. 2018

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“…Several implementations of Apriori have already been done; each of which goes through a number of iterations of the MapReduce task as long as the size of frequent itemset [13,14,15,16,17,18]. FP-Growth is also used with MapReduce [19,20] and Spark [21] for addressing the same concerned problem. Again, there are the cases where a combination of Apriori and FP-Growth [22] leads to better results.…”

Section: Related Workmentioning

confidence: 99%

Pattern mining for large distributed dataset: A parallel approach (PMLDD)

Pal¹,

Kumar²

2018

TIIS

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Handling vast amount of data found in large transactional datasets is an obvious challenge for the conventional data mining algorithms. Addressing this challenge, our paper proposes a parallel approach for proper decomposition of mining problem into sub-problems in order to find frequent patterns from these datasets. The proposed, Pattern Mining for Large Distributed Dataset (PMLDD) approach, ensures minimum dependencies as well as minimum communications among sub-problems. It establishes a linear aggregation of the intermediate results so that it can be adapted to large-scale programming models like MapReduce. In this context, an algorithmic structure for MapReduce programming model is presented. PMLDD guarantees an efficient load balancing among the sub-problems by a specific selection criterion. Further, it optimizes the number of required iterations over the dataset for mining frequent patterns as compared to the existing approaches. Finally, we believe that our approach is scalable enough to handle larger datasets in terms of performance evaluation, and the result analysis justifies all these mentioned concerns.

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“…A different incremental data mining algorithm has been proposed by Chang et al [4]. The proposed method is based on FP-Growth and uses the concept of heap tree for incrementally updating the frequent itemsets.…”

Section: 1mentioning

confidence: 99%

“…Association Rules (ARs) mining [4] is a data mining procedure for identifying frequent associations in data. Classical association rules capture frequent co-occurrences of attribute values, while ignoring any possible frequent relation between attribute values.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Gradual Relational Association Rules Mining Approach

Miholca

2018

Studia UBB Informatica

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This paper focuses on adaptive Gradual Relational Association Rules mining. Gradual Relational Association Rules capture gradual generic relations among data features. We propose AGRARM , an algorithm for mining the interesting Gradual Relational Association Rules characterizing a data set that has been extended with a number of new attributes, through adapting the set of interesting rules mined before extension, so as to preserve the completeness. We aim, through AGRARM , to make the mining process more efficient than resuming the mining algorithm on the enlarged data. We have experimentally evaluated AGRARM versus mining from scratch on three publicly available data sets. The obtained reduction in mining time highlights AGRARM 's efficiency, thus confirming the potential of our proposal.

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A Novel Incremental Data Mining Algorithm Based on FP-growth for Big Data

Cited by 26 publications

References 8 publications

An improvement of FP-Growth association rule mining algorithm based on adjacency table

An improvement of FP-Growth association rule mining algorithm based on adjacency table

Pattern mining for large distributed dataset: A parallel approach (PMLDD)

An Adaptive Gradual Relational Association Rules Mining Approach

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