Toward boosting distributed association rule mining by data de-clustering

Tseng, Frank S. C.; Kuo, Yu‐Hsuan; Huang, Yueh‐Min

doi:10.1016/j.ins.2010.07.020

Cited by 13 publications

(4 citation statements)

References 45 publications

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“…The use of distributed parallel architectures suffers from an overhead in inter-site communications among processors during frequent itemsets generation (Tseng et al,2010) on the other side, the use of Master/Slaves scheme reduces significantly the number of communications /synchronizations required for the distributed computation of global frequent Itemsets, for instance in work (Vasoya and Koli,2016) the proposed system including scheme Master/Slaves gives better time complexity and space complexity, at first the Master processor partitions the whole database into different clusters and distributes the clusters to Slave processors (Tassa, 2013). Each Slave generates frequent itemsets using improve Apriori algorithm and submit frequent itemsets to the Master processor.…”

Section: Reduce the Inter-sites Communication Costmentioning

confidence: 99%

An Efficient Method for Mining Distributed Frequent Itemsets: MDFI

al.¹

2021

TURCOMAT

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Discovering frequent Itemsets is an interesting problem in the context of parallel and distributed databases. Computation cost and communication/synchronization overhead are important elements in distributed Frequent Itemsets. In this work, we propose an efficient algorithm for mining distributed frequent Itemsets (MDFI) which can significantly reduce the number of candidates Itemsets and communication costs by adopting a Master/Slaves scheme of communication. We present performance comparisons for our algorithm against Apriori and FP-growth algorithms using two datasets with different minimum support.

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Section: Reduce the Inter-sites Communication Costmentioning

confidence: 99%

An Efficient Method for Mining Distributed Frequent Itemsets: MDFI

al.¹

2021

TURCOMAT

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“…However, beyond providing an alternative theoretical way of understanding and measuring information as a representational and cognitive quantity, the notion of representational information and its measurement may be useful in more pragmatic settings. Some potential applications of the present theory include: (1) database analysis, (2) rule mining as in [27], (3) modeling and implementation of conceptual processes in artificial and human cognitive systems (e.g., artificial classifiers as in [10], robot perception as in [12], and AI experts), and 4) information compression.…”

Section: Potential Applicationsmentioning

confidence: 99%

Representational information: a new general notion and measure of information

Vigo

2011

Information Sciences

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a b s t r a c tIn what follows, we introduce the notion of representational information (information conveyed by sets of dimensionally defined objects about their superset of origin) as well as an original deterministic mathematical framework for its analysis and measurement. The framework, based in part on categorical invariance theory [30], unifies three key constructs of universal science -invariance, complexity, and information. From this unification we define the amount of information that a well-defined set of objects R carries about its finite superset of origin S, as the rate of change in the structural complexity of S (as determined by its degree of categorical invariance), whenever the objects in R are removed from the set S. The measure captures deterministically the significant role that context and category structure play in determining the relative quantity and quality of subjective information conveyed by particular objects in multi-object stimuli.

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“…The problem of frequent itemset mining [1][2][3]7,14,[26][27][28] is discovering the complete set of itemsets that appear with high frequency in transactional databases. However, the utility of the itemsets is not considered in ordinary frequent itemset mining.…”

Section: Introductionmentioning

confidence: 99%