An Efficient Algorithm for Mining Frequent Itemsets and Association Rules

2020 ◽  
pp. 229-244
Author(s):  
Sallam Fageeri ◽  
Rohiza Ahmad ◽  
Hitham Alhussian
Keyword(s):  
Association Rules ◽  
Efficient Algorithm ◽  
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.

INFIN: An Efficient Algorithm for Fast Mining Frequent Itemsets

2021 ◽  
Author(s):  
ShaoPeng Wang ◽  
YuFei Wang ◽  
ChunKai Feng ◽  
ChaoYu Niu
Keyword(s):  
Efficient Algorithm ◽  
Frequent Itemsets ◽  
Mining Frequent Itemsets

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.

FCILINK: Mining Frequent Closed Itemsets Based on a Link Structure between Transactions

2005 ◽  
Vol 04 (04) ◽  
pp. 257-267
Author(s):  
Kyong Rok Han ◽  
Jae Yearn Kim
Keyword(s):  
Association Rules ◽  
Efficient Algorithm ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Link Structure ◽  
The Past ◽  
Large Databases ◽  
Closure Mechanism ◽  
Closed Itemsets ◽  
Significant Patterns

The problem of discovering association rules between items in a database is an emerging area of research. Its goal is to extract significant patterns or interesting rules from large databases. Recent studies of mining association rules have proposed a closure mechanism. It is no longer necessary to mine the set of all of the frequent itemsets and their association rules. Rather, it is sufficient to mine the frequent closed itemsets and their corresponding rules. In the past, a number of algorithms for mining frequent closed itemsets have been based on items. In this paper, we use the transaction itself for mining frequent closed itemsets. An efficient algorithm called FCILINK is proposed that is based on a link structure between transactions. A given database is scanned once and then a much smaller sub-database is scanned twice. Our experimental results show that our algorithm is faster than previously proposed methods. Furthermore, our approach is significantly more efficient for dense databases.

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