scholarly journals Mining Frequent Itemsets of Novel Characters Based on Association Rules

2020 ◽  
Vol 1550 ◽  
pp. 032158
Author(s):  
Hui Cao ◽  
Jiao Li
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
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 MINING ESSENTIAL RULES USING FREQUENT CLOSED ITEMSETS LATTICE

2009 ◽  
Vol 12 (11) ◽  
pp. 49-56
Author(s):  
Bac Hoai Le ◽  
Bay Dinh Vo
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Suitable Method ◽  
Mining Method ◽  
Parent Child Relationship ◽  
Left Hand ◽  
Child Relationship ◽  
Closed Itemsets ◽  
The Cost ◽  
Parent Child

In traditional mining of association rules, finding all association rules from databases that satisfy minSup and minConf faces with some problems in case of the number of frequent itemsets is large. Thus, it is necessary to have a suitable method for mining fewer rules but they still embrace all rules of traditional mining method. One of the approaches that is the mining method of essential rules: it only keeps the rule that its left hand side is minimal and its right side is maximal (follow in parent-child relationship). In this paper, we propose a new algorithm for mining the essential rules from the frequent closed itemsets lattice to reduce the time of mining rules. We use the parent-child relationship in lattice to reduce the cost of considering parent-child relationship and lead to reduce the time of mining rules.

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