Hash Partitioned apriori in Parallel and Distributed Data Mining Environment with Dynamic Data Allocation Approach

Paul, Sujni; Saravanan, V.

doi:10.1109/iccsit.2008.63

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…Conventional Apriori and FP-Growth algorithms for finding frequent-sequence patterns are inefficient for large datasets. Therefore, some parallel versions of these algorithms have been proposed [10–13]. However, parallel implementations of the conventional, frequent-sequence pattern-mining algorithms also suffer from inherent limitations of parallel and distributed computing architecture like data partitioning and distribution, job assignment and monitoring, load balancing and so on.…”

Section: Related Workmentioning

confidence: 99%

Fast prediction of web user browsing behaviours using most interesting patterns

Sisodia

Khandal

Singhal

2016

Journal of Information Science

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The prediction of users’ browsing behaviours is essential for putting appropriate information on the web. The browsing behaviours are stored as navigational patterns in web server logs. These weblogs are used to predict the frequently accessed patterns of web users, which can be used to predict user behaviour and to collect business intelligence. However, owing to the exponentially increasing weblog size, existing implementations of frequent-pattern-mining algorithms often take too much time and generate too many redundant patterns. This article introduces the most interesting pattern-based parallel FP-growth (MIP-PFP) algorithm. MIP-PFP is an improved implementation of the parallel FP-growth algorithm and implemented on the Apache Spark platform for extracting frequent patterns from huge weblogs. Experiments were performed on openly available National Aeronautics and Space Administration (NASA) weblog data to test the effectiveness of the MIP-PFP algorithm. The results were compared with existing implementation of PFP algorithms. The results suggest that the MIP-PFP algorithm running on Apache Spark reduced the execution time by a factor of more than 10 times. The effect of sequence length that has been used as input to the MIP-PFP algorithm was also evaluated with different interestingness parameters including support, confidence, lift, leverage, cosine, and conviction. It is observed from experimental results that only sequences of length greater than three produced a very low value of support for these interestingness measures.

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Section: Related Workmentioning

confidence: 99%

Fast prediction of web user browsing behaviours using most interesting patterns

Sisodia

Khandal

Singhal

2016

Journal of Information Science

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“…On the other hand, many algorithms have devoted to perform ARM in parallel and/or distributed data mining, such as HPA [3], WDPA [5], Apriori-T [9], etc. HDDS [10] implements ARM on grid environments, and some works have been done in peer-to-peer system.…”

Section: Related Workmentioning

confidence: 99%

“…Association rules mining (ARM) is one of the most useful techniques. The challenges associated with ARM, especially for parallel and distributed data mining, include minimizing I/O, increasing processing speed and reducing communication cost [3]. A major concern in ARM today is to continue to improve algorithm performance.…”

Section: Introductionmentioning

confidence: 99%

An Improved Apriori-based Algorithm for Association Rules Mining

Lü

Lin

et al. 2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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Because of the rapid growth in worldwide information, efficiency of association rules mining (ARM) has been concerned for several years. In this paper, based on the original Apriori algorithm, an improved algorithm IAA is proposed. IAA adopts a new count-based method to prune candidate itemsets and uses generation record to reduce total data scan amount. Experiments demonstrate that our algorithm outperforms the original Apriori and some other existing ARM methods.

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Parallel Association Rule Mining for Medical Applications

Zhang

Sheu

et al. 2011

2011 IEEE 11th International Conference on Bioinformatics and Bioengineering

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Hash Partitioned apriori in Parallel and Distributed Data Mining Environment with Dynamic Data Allocation Approach

Cited by 11 publications

References 8 publications

Fast prediction of web user browsing behaviours using most interesting patterns

Fast prediction of web user browsing behaviours using most interesting patterns

An Improved Apriori-based Algorithm for Association Rules Mining

Parallel Association Rule Mining for Medical Applications

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