Fastest association rule mining algorithm predictor (FARM-AP)

Hooshsadat, Metanat; Samuel, Hamman; Patel, Sonal; Zaı̈ane, Osmar R.

doi:10.1145/1992896.1992902

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…If a rule contains quantitative attributes either in antecedent or consequent, then the rule is termed as quantitative association rule (Srikant & Agrawal, 1996;Moreno, Segrera, Lopez, & Polo, 2006). Some efficient algorithms for association rule mining proposed in the literature are: APRIORI (Agrawal & Srikant, 1994), PRICES (Wang & Tjortjis, 2004), ChARM (Zaki & Hsiao, 1999) and CLOSET (Brin, Motwani, & Silverstein, 1997), Elcat (Zaki, 2000), FPgrowth (Han, Pei, Yin, & Mao, 2004), OPUS (Webb, 1995), GUHA (Hajek, Havel, & Chytil, 1966) and FARM-AP (Hooshsadat, Samuel, Patel, & Zalance, 2011). Some algorithms used for frequent item sets generation are: MAFIA (Burdick, Calimlim, Flannick, Gehrke, & Yiu, 2005), TFP (Jianyong, Han, Lu, & Tzvetkov, 2005) and estMax (Woo & Lee, 2009).…”

Section: Association Rule Miningmentioning

confidence: 99%

A Survey on Fuzzy Association Rule Mining

Kalia¹,

Dehuri

Ghosh

2013

International Journal of Data Warehousing and Mining

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Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

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Section: Association Rule Miningmentioning

confidence: 99%

A Survey on Fuzzy Association Rule Mining

Kalia¹,

Dehuri

Ghosh

2013

International Journal of Data Warehousing and Mining

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“…There is a wide spectrum of algorithms for frequent itemset mining, and none of them outperforms all others for all possible transactional databases and values of minsup threshold [9]. Apriori [1] is one of the most popular itemset mining algorithms, for which many refinements and parallel implementations for various platforms were proposed.…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems

Zymbler

2019

CIT. J. comput. inf. technol

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Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases. Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation of candidate itemsets based on frequent itemsets found at the previous iteration, and pruning of clearly infrequent itemsets. The Dynamic Itemset Counting (DIC) algorithm is a variation of Apriori, which tries to reduce the number of passes made over a transactional database while keeping the number of itemsets counted in a pass relatively low. In this paper, we address the problem of accelerating DIC on the Intel Xeon Phi many-core system for the case when the transactional database fits in main memory. Intel Xeon Phi provides a large number of small compute cores with vector processing units. The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism. We exploit the bit-based internal layout for transactions and itemsets. This technique reduces the memory space for storing the transactional database, simplifies the support count via logical bitwise operation, and allows for vectorization of such a step. Experimental evaluation on the platforms of the Intel Xeon CPU and the Intel Xeon Phi coprocessor with large synthetic and real databases showed good performance and scalability of the proposed algorithm.

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“…Most of the conventional association rule mining algorithms (HooshSadat et al, 2011;Chi, 2012;Srikant and Agrawal, 1996;Coenen et al, 2004;Sekhavat et al, 2010) are competent to handle Boolean or binary data. These algorithms mine rules from quantitative data as well, by first partitioning into interval and subsequently converting them into Boolean type.…”

Section: Introductionmentioning

confidence: 99%

On the mining of fuzzy association rule using multi-objective genetic algorithms

Kalia¹,

Dehuri

Ghosh

et al. 2016

IJDMMM

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The discovery of association rule acquire an imperative role in data mining since its inception, which tries to find correlation among the attributes in a database. Classical algorithms/procedures meant for Boolean data and they suffer from sharp boundary problem in handling quantitative data. Thereby fuzzy association rule (i.e., association rule based on fuzzy sets) with fuzzy minimum support and confidence is introduced as an alternative tool. Besides, rule length, comprehensibility, and interestingness are also potentially used as quality metrics. Additionally, in fuzzy association rule mining, determining number fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence fuzzy association rule mining problem can be viewed as a multi-objective optimisation problem. On the other side, multi-objective genetic algorithms are established and efficient techniques to uncover Pareto front. Therefore, to bridge these two fields of research many

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Fastest association rule mining algorithm predictor (FARM-AP)

Cited by 10 publications

References 17 publications

A Survey on Fuzzy Association Rule Mining

A Survey on Fuzzy Association Rule Mining

Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems

On the mining of fuzzy association rule using multi-objective genetic algorithms

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