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Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach

Pamli Basak, R. R. Sedamkar, Rashmi Thakur Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
10.5120/ijais15-451369
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  1. Pamli Basak, R r Sedamkar and Rashmi Thakur. Article: Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach. International Journal of Applied Information Systems 9(2):6-10, June 2015. BibTeX

    @article{key:article,
    	author = "Pamli Basak and R.r. Sedamkar and Rashmi Thakur",
    	title = "Article: Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 2,
    	pages = "6-10",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Datasets grow in size as they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial, software logs, microphones, wireless sensor networks and cameras. This paper presents a structure for simply, easily and competently parallelizing data mining algorithms for those huge datasets together with the incremental mining. MapReduce concept is use to execute the parallel FP-Growth algorithm by running the windows services parallel. The proposed algorithm eliminates duplicated work and spurious items. Also, it shortens the response time to a query for the set of frequent items. The proposed algorithm is implemented by parallel running of many windows services and experimental results shows tremendous advantages. The proposed algorithm runs 66% faster than the traditional algorithm of data mining. Also, memory utilization reduces by 37%.

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Keywords

Incremental Data Mining, Parallel FP-growth, MapReduce jobs, Incremental Parallel FP-growth.