Effective algorithm of mining frequent itemsets for association rules

2005 ◽  
Author(s):  
Pei-Qi Liu ◽  
Zeng-Zhi Li ◽  
Yin-Liang Zhao
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Effective Algorithm ◽  
Mining Frequent Itemsets

A Scalable Vertical Model for Mining Association Rules

2004 ◽  
Vol 03 (04) ◽  
pp. 317-329 ◽  
Author(s):  
Imad Rahal ◽  
Dongmei Ren ◽  
William Perrizo
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Monotonicity Property ◽  
Frequent Itemsets ◽  
Large Datasets ◽  
Rule Mining ◽  
Very Large Datasets ◽  
Computationally Intensive ◽  
Downward Closure ◽  
Mining Frequent Itemsets

Association rule mining (ARM) is the data-mining process for finding all association rules in datasets matching user-defined measures of interest such as support and confidence. Usually, ARM proceeds by mining all frequent itemsets — a step known to be very computationally intensive — from which rules are then derived in a straight forward manner. In general, mining all frequent itemsets prunes the space by using the downward closure (or anti-monotonicity) property of support which states that no itemset can be frequent unless all of its subsets are frequent. A large number of papers have addressed the problem of ARM but not many of them have focused on scalability over very large datasets (i.e. when datasets contain a very large number of transactions). In this paper, we propose a new model for representing data and mining frequent itemsets that is based on the P-tree technology for compression and faster logical operations over vertically structured data and on set enumeration trees for fast itemset enumeration. Experimental results presented hereinafter show big improvements for our approach over large datasets when compared to other contemporary approaches in the literature.

Research on Data Mining Based on Apriori Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1675-1679
Author(s):  
Pei Ji Wang ◽  
Yu Lin Zhao
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
Frequent Itemsets ◽  
Scientific Data ◽  
Apriori Algorithm ◽  
Data Table ◽  
Large Databases ◽  
Mining Association Rules ◽  
Mining Frequent Itemsets

With the availability of inexpensive storage and the progress in data collection tools, many organizations have created large databases of business and scientific data, which create an imminent need and great opportunities for mining interesting knowledge from data.Mining association rules is an important topic in the data mining research. In the paper, research mining frequent itemsets algorithm based on recognizable matrix and mining association rules algorithm based on improved measure system, the above method is used to mine association rules to the students’ data table under Visual FoxPro 6.0.

The Optimization Algorithm of Association Rules Mining

2014 ◽  
Vol 614 ◽  
pp. 405-408
Author(s):  
Zhen Yu Liu ◽  
Zhi Hui Song ◽  
Rui Qing Yan ◽  
Zeng Zhang
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Decision Makers ◽  
Generation Process ◽  
Association Rules Mining ◽  
Rule Sets ◽  
Speed Up ◽  
Frequent Itemsets Mining ◽  
Encoding Method ◽  
Mining Frequent Itemsets

Frequent itemsets mining is the core part of association rule mining. At present most of the research on association rules mining is focused on how to improve the efficiency of mining frequent itemsets , however, the rule sets generated from frequent itemsets are the final results presented to decision makers for making, so how to optimize the rulesets generation process and the final rules is also worthy of attention. Based on encoding the dataset, this paper proposes a encoding method to speed up the generation process of frequent itemsets and proposes a subset tree to generate association rules which can simplify the generation process of rules and narrow the rulesets presented to decision makers.

scholarly journals NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Huy Quang Pham, Duc Tran, Ninh Bao Duong, Philippe Fournier-Viger, Alioune Ngom
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Experimental Results ◽  
Extra Cost ◽  
Effective Algorithm ◽  
Closed Frequent Itemsets ◽  
Mining Frequent Itemsets

Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs.

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