NDLI: Incremental FP-Growth mining strategy for dynamic threshold value and database based on MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Xiaoting Wei Yunlong Ma Feng Zhang Min Liu Weiming Shen
Copyright Year	2014
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China (Xiaoting Wei; Yunlong Ma; Feng Zhang; Min Liu; Weiming Shen)
Abstract	With the coming of the Big Data era, data mining has been confronted with new opportunities and challenges. Some limitations are exposed when traditional association rule mining algorithms are used to deal with large-scale data. In the Apriori algorithm, scanning the external storage repeatedly leads to high I/O load and brings about low performance. As for FP-Growth algorithm, the effectiveness is limited by internal memory size because mining process is on the base of large tree-form data structure. What's more, although remarkable achievements have been scored, there are still problems in dynamic scenarios. The paper presents a parallelized incremental FP-Growth mining strategy based on MapReduce, which aims to process large-scale data. The proposed incremental algorithm realizes effective data mining when threshold value and original database change at the same time. This novel algorithm is implemented on Hadoop and shows great advantages according to the experimental results.
Sponsorship	IEEE SMC Soc.
Starting Page	271
Ending Page	276
File Size	646045
Page Count	6
File Format	PDF
ISBN	9781479937769
DOI	10.1109/CSCWD.2014.6846854
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Itemsets Association rules Heuristic algorithms Algorithm design and analysis Classification algorithms MapReduce Association rule mining FP-Growth algorithm threshold value dynamic database
Content Type	Text
Resource Type	Article

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