Effect of data skewness in parallel mining of association rules

Cheung, David W.; Xiao, Yajie

doi:10.1007/3-540-64383-4_5

Cited by 42 publications

(25 citation statements)

References 12 publications

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“…At each level and on each machine, the database scan is accomplished independently on the local partition. Distributed Pruning is then done.FPM (Fast Parallel Mining) for Association rule mining has been proposed [26] in 1998. It adopts Count Distribution method and has incorporated two powerful candidate trimming techniques.…”

Section: Parallel Association Rule Miningmentioning

confidence: 99%

A Study on Milestones of Association Rule Mining Algorithms in Large Databases

Suba¹,

T²

2012

IJCA

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Data mining helps in doing automated extraction and generating predictive information from large amount of data. The association rule mining is one of the important area of research in Data mining. The Association rule mining identifies the useful associations or relationship among big set of data items. In this paper, we provide the important concepts of Association rule mining and existing algorithms and their effectiveness and drawbacks. The references provided in this paper covered the main theoretical issues and guiding the researcher in an interesting research directions that have yet to be discovered.

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Section: Parallel Association Rule Miningmentioning

confidence: 99%

A Study on Milestones of Association Rule Mining Algorithms in Large Databases

Suba¹,

T²

2012

IJCA

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“…The results are transmitted back to the local databases. In some approaches [5,6,8], instead of a merger site, the localmodels are broadcasted to all other sites, so that eachsite can in parallel compute the global model. The distribution aspect of FIM can be described as follows.…”

Section: Problem Definitionmentioning

confidence: 99%

“…CD, FDM, FPM and DDM [5,6,8,9] parallelize Apriori [10], PDM [12] parallelizes DHP [11], D-Sampling [7] is a combination of serialsampling approach [22] and DDM algorithm, parallel FPgrowth [13] is a parallelized version of FP-growth [14], and so on.As mentioned before, many frequent itemsets mining algorithms, both sequential and distributed, are related to the Apriori algorithm [10]. The name of the algorithm is based on the fact that it uses prior knowledge of frequent itemsets properties.…”

Section: Previous Workmentioning

confidence: 99%

“…Therefore, a most important problem of distributed architectures and algorithms is overheads cost in inter-site communications among processors. Some of the distributed algorithms like CD [5], FDM [6], and FPM [8] do not have any merger site and message exchange is all to all which is not a strong architecture. But, DDM [9] and ODAM [15] have used a merger site to collect local counts from other sites.…”

Section: Introductionmentioning

confidence: 99%

“…However, there could be a lot of associations among the data which may not be able to deduce from common knowledge. Therefore, since association ruless inception, many sequential and distributed frequent itemset mining algorithms have been proposed in the literature [5][6][7][8][9][10][11][12][13][14][15], etc. Many of them are correlated to the Apriori algorithm [10] which is a well-known method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Dynamic Distributed Algorithm for Frequent Itemsets Mining

Adelpoor¹,

Abadeh²

2013

IJCA

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Mining for association rules between items in large transactional databases is a central problem in the field of knowledge discovery. It has crucial applications in decision support and marketing strategy. Centralized and Distributed Association Rules Mining (DARM) include two phases of frequent itemset extraction and strong rule generation. The most important part of ARM is Frequent Itemsets Mining (FIM)and because of its importance in recent years, there have been many algorithms implemented for it. In this paper, we have focused on distributed Apriori-Like frequent itemsets mining and proposed a distributed algorithm, called New Dynamic Distributed Frequent Itemsets Mining (NDD-FIM), for geographically distributed data sets. NDD-FIM has a merger site to reduce communication overhead and eliminates size of dataset partitions dynamically. The experimental results show that our algorithm generates support counts of candidate itemsets quickerthan other DARM algorithms and reduces the size of average transactions, datasets, and messageexchanges.

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Incremental association rule mining: a survey

Nath

Bhattacharyya

Ghosh

2013

WIREs Data Min & Knowl

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Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state‐of‐the‐art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification

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Effect of data skewness in parallel mining of association rules

Cited by 42 publications

References 12 publications

A Study on Milestones of Association Rule Mining Algorithms in Large Databases

A Study on Milestones of Association Rule Mining Algorithms in Large Databases

A New Dynamic Distributed Algorithm for Frequent Itemsets Mining

Incremental association rule mining: a survey

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