Mining Frequent Itemsets in Association Rule Mining Using Improved SETM Algorithm

Author(s):  
D. Kerana Hanirex ◽  
K. P. Kaliyamurthie
2018 ◽  
Vol 189 ◽  
pp. 10012 ◽  
Author(s):  
Ming Yin ◽  
Wenjie Wang ◽  
Yang Liu ◽  
Dan Jiang

FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.


2014 ◽  
Vol 556-562 ◽  
pp. 3501-3505
Author(s):  
Zi Zhi Lin ◽  
Si Hui Shu

Association rule mining is one of the most important and well researched techniques of data mining. The key procedure of the association rule mining is to find frequent itemsets. In this paper, a new mining frequent itemsets algorithm based on matrix is introduced. Frequent itemsets are obtained by compressing the transaction matrix efficiently by a new strategy. The new algorithm optimizes the known mining frequent itemsets algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity. It is more feasible especially when the degrees of the frequent itemsets are high.


2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.


2012 ◽  
Vol 6-7 ◽  
pp. 625-630 ◽  
Author(s):  
Hong Sheng Xu

In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.


Author(s):  
Mafruz Ashrafi ◽  
David Taniar ◽  
Kate Smith

Association rule mining is one of the most widely used data mining techniques. To achieve a better performance, many efficient algorithms have been proposed. Despite these efforts, many of these algorithms require a large amount of main memory to enumerate all frequent itemsets, especially when the dataset is large or the user-specified support is low. Thus, it becomes apparent that we need to have an efficient main memory handling technique, which allows association rule mining algorithms to handle larger datasets in the main memory. To achieve this goal, in this chapter we propose an algorithm for vertical association rule mining that compresses a vertical dataset in an efficient manner, using bit vectors. Our performance evaluations show that the compression ratio attained by our proposed technique is better than those of the other well-known techniques.


2014 ◽  
Vol 998-999 ◽  
pp. 899-902 ◽  
Author(s):  
Cheng Luo ◽  
Ying Chen

Existing data miming algorithms have mostly implemented data mining under centralized environment, but the large-scale database exists in the distributed form. According to the existing problem of the distributed data mining algorithm FDM and its improved algorithms, which exist the problem that the frequent itemsets are lost and network communication cost too much. This paper proposes a association rule mining algorithm based on distributed data (ARADD). The mapping marks the array mechanism is included in the ARADD algorithm, which can not only keep the integrity of the frequent itemsets, but also reduces the cost of network communication. The efficiency of algorithm is proved in the experiment.


2004 ◽  
Vol 03 (03) ◽  
pp. 245-257 ◽  
Author(s):  
Kwang-Il Ahn ◽  
Jae-Yearn Kim

Association rule mining is an important research topic in data mining. Association rule mining consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. In the first step, efficiency is important since discovering frequent itemsets is computationally time consuming. In the second step, unbiased assessment is important for good decision making. In this paper, we deal with both the efficiency of the mining algorithm and the measure of interest of the resulting rules. First, we present an algorithm for finding frequent itemsets that uses a vertical database. We also introduce a modified vertical data format to reduce the size of the database and an itemset reordering strategy to reduce the size of the intermediate tidsets. Second, we present a new measure to evaluate the interest of the resulting association rules. Our performance analysis shows that our proposed algorithm reduces the size of the intermediate tidsets that are generated during the mining process. The smaller tidsets make intersection operations faster. Using our interest-measuring test helps to avoid the discovery of misleading rules.


2013 ◽  
Vol 327 ◽  
pp. 197-200
Author(s):  
Guo Fang Kuang ◽  
Ying Cun Cao

The material is used by humans to manufacture the machines, components, devices and other products of substances. Association rules originated in the field of data mining, people use it to find large amounts of data between itemsets of the association. Apriori is a breadth-first algorithm to obtain the support is greater than the minimum support of frequent itemsets by repeatedly scanning the database. This paper presents the construction of materials science and information model based on association rule mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.


2014 ◽  
Vol 685 ◽  
pp. 575-578
Author(s):  
Guang Jiang Wang ◽  
Shi Guo Jin

Association rule mining is an important data mining method; it is the key link of finding frequent itemsets. The process of association rules mining is roughly into two steps: the first step is to find out from all the concentration of all the frequent itemsets; the second step is to obtain the association rules from frequent itemsets. This paper analyzes the collected information of nodes in wireless sensor network and management. The paper presents application of association rule mining technology in the collection and management of wireless sensor network node.


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