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.

E-MsNFIS: Efficient Negative Frequent Itemsets Mining Based on Multiple Minimum Supports

2013 ◽  
Vol 411-414 ◽  
pp. 386-389 ◽  
Author(s):  
Tian Tian Xu ◽  
Xiang Jun Dong
Keyword(s):  
Association Rules ◽  
Real Life ◽  
Frequent Itemsets ◽  
Negative Association ◽  
Experimental Results ◽  
New Method ◽  
Minimum Support ◽  
Frequent Itemsets Mining ◽  
Negative Association Rules ◽  
Multiple Minimum Supports

Negative frequent itemsets (NFIS) like (a1a2¬a3a4) have played important roles in real applications because we can mine valued negative association rules from them. In one of our previous work, we proposed a method, namede-NFISto mine NFIS from positive frequent itemsets (PFIS). However,e-NFISonly uses single minimum support, which implicitly assumes that all items in the database are of the same nature or of similar frequencies in the database. This is often not the case in real-life applications. So a lot of methods to mine frequent itemsets with multiple minimum supports have been proposed. These methods allow users to assign different minimum supports to different items. But these methods only mine PFIS, doesn’t consider negative ones. So in this paper, we propose a new method, namede-msNFIS, to mine NFIS from PFIS based on multiple minimum supports. E-msNFIScontains three steps: 1) using existing methods to mine PFIS with multiple minimum supports; 2) using the same method ine-NFISto generate NCIS from PFIS got in step 1; 3) calculating the support of these NCIS only using the support of PFIS and then gettingNFIS. Experimental results show that thee-msNFISis efficient.

The Association Rules Updating Algorithm Based on Reverse Search

2011 ◽  
Vol 467-469 ◽  
pp. 1126-1131
Author(s):  
Yu Chen ◽  
Wei Xiang Xu ◽  
Xu Min Liu
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Decision Makers ◽  
Maximal Frequent Itemsets ◽  
Search Approach ◽  
The Cost

This paper analyzed the existing association rules update algorithm IUA, found out that when the decision makers gave priority attention to the situation of maximum frequent itemsets, this algorithm cannot lower the cost of the database traversal to quickly access to the largest number of frequent itemsets. For the lack of the algorithm, an algorithm which is based on reverse search approach to update association rules is presented. The updating algorithm based on reverse search first generated all frequent itemsets of new itemsets. Then, it spliced the new largest frequent itemsets and original largest frequent itemsets for trimming, get the updated maximal frequent itemsets. This algorithm not only reduces the traversal times in the process of association rules updating, but also realized the priority access to the largest operation of frequent itemsets.

An Improved Multi-Parameters Perturbation Privacy Preserving Association Rules Mining Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 2629-2632
Author(s):  
Jie Liu ◽  
Tian Qi Li ◽  
Jian Pei Zhang
Keyword(s):  
Association Rules ◽  
Original Data ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Data Perturbation ◽  
First Line ◽  
Time Efficiency ◽  
Association Rules Mining ◽  
Original Algorithm ◽  
Parameters Perturbation

Multi-parameters data perturbation method is a kind of original data perturbation methods for privacy preserving association rules mining. However, the time-efficiency of restoring the frequent itemsets in multi-parameters perturbation algorithm is still not high.One method is proposed in this paper to improve the time efficiency of multi-parameters randomized perturbation algorithm according to the characteristics of the model to restore frequent itemsets. The method improves the time efficiency by getting the elements of the first line of the inversed matrix of transformation matrix. Finally, both theoretical analysis and experimental results show that the improved algorithm is more time-efficient and space-efficient than the original algorithm.

Research into the Algorithm of Frequent Pattern Mining Based on across Linker

2012 ◽  
Vol 195-196 ◽  
pp. 984-986
Author(s):  
Ming Ru Zhao ◽  
Yuan Sun ◽  
Jian Guo ◽  
Ping Ping Dong
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Important Data ◽  
Classical Algorithm ◽  
Frequent Itemsets Mining ◽  
Mining Frequent Itemsets

Frequent itemsets mining is an important data mining task and a focused theme in data mining research. Apriori algorithm is one of the most important algorithm of mining frequent itemsets. However, the Apriori algorithm scans the database too many times, so its efficiency is relatively low. The paper has therefore conducted a research on the mining frequent itemsets algorithm based on a across linker. Through comparing with the classical algorithm, the improved algorithm has obvious advantages.

scholarly journals A novel collaborative filtering algorithm by bit mining frequent itemsets

2018 ◽  
Author(s):  
Loc Nguyen ◽  
Minh-Phung T. Do
Keyword(s):  
Collaborative Filtering ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Storage Space ◽  
Model Based ◽  
Collaborative Filtering Algorithm ◽  
Speed Up ◽  
Mining Frequent Itemsets ◽  
Bitwise Operations ◽  
Process Mode

Collaborative filtering (CF) is a popular technique in recommendation study. Concretely, items which are recommended to user are determined by surveying her/his communities. There are two main CF approaches, which are memory-based and model-based. I propose a new CF model-based algorithm by mining frequent itemsets from rating database. Hence items which belong to frequent itemsets are recommended to user. My CF algorithm gives immediate response because the mining task is performed at offline process-mode. I also propose another so-called Roller algorithm for improving the process of mining frequent itemsets. Roller algorithm is implemented by heuristic assumption “The larger the support of an item is, the higher it’s likely that this item will occur in some frequent itemset”. It models upon doing white-wash task, which rolls a roller on a wall in such a way that is capable of picking frequent itemsets. Moreover I provide enhanced techniques such as bit representation, bit matching and bit mining in order to speed up recommendation process. These techniques take advantages of bitwise operations (AND, NOT) so as to reduce storage space and make algorithms run faster.

An Efficient Weighted Association Rules Mining Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1247-1250 ◽  
Author(s):  
Na Xin Peng
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Mining Algorithm ◽  
Pruning Strategy

Aiming at the problem that most of weighted association rules algorithm have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, Boolean weighted association rules algorithm and weighted fuzzy association rules algorithm are presented, which use pruning strategy of Apriori algorithm so as to improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

An Approach to the Mining of User Focused Frequent Itemsets Based on Attention

2014 ◽  
Vol 536-537 ◽  
pp. 520-523
Author(s):  
Jia Liu ◽  
Zhen Ya Zhang ◽  
Hong Mei Cheng ◽  
Qian Sheng Fang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Frequent Itemsets ◽  
Selection Model ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Information Filter ◽  
Frequent Itemsets Mining ◽  
Log File

Usually, non trivial network visiting behaviors implied in network visiting log can be treated as the frequent itemsets or association rules if data in networking log file are transformed into transaction and technologies on association rule can be used to mine those frequent itemsets which are focused by user or some application. To mine non trivial behaviors of network visiting effectively, an attention based frequent itemsets mining method is proposed in this paper. In our proposed method, properties of users focusing is described as attention set and the early selection model of attention as information filter is referenced in the design of our method. Experimental results show that our proposed method is faster than apriori algorithm on the mining of frequent itemsets which is focused by our attention.

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