An Improved Algorithm for Mining Top-k Association Rules

2017 ◽  
pp. 117-128
Author(s):  
Linh T. T. Nguyen ◽  
Loan T. T. Nguyen ◽  
Bay Vo
Keyword(s):  
Association Rules ◽  
Improved Algorithm

Improved Apriori Algorithm in the Power System Fault

2014 ◽  
Vol 556-562 ◽  
pp. 1510-1514
Author(s):  
Li Qiang Lin ◽  
Hong Wen Yan
Keyword(s):  
Power System ◽  
Association Rules ◽  
Fault Analysis ◽  
The Other ◽  
Apriori Algorithm ◽  
Extreme Climate ◽  
Time Space ◽  
Low Efficiency ◽  
Candidate Item ◽  
Improved Algorithm

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

A Novel and Improved Apriori Algorithm

2014 ◽  
Vol 721 ◽  
pp. 543-546 ◽  
Author(s):  
Dong Juan Gu ◽  
Lei Xia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Inner Product ◽  
Apriori Algorithm ◽  
Product Operation ◽  
Classical Algorithm ◽  
Transaction Database ◽  
The Given ◽  
To Receive ◽  
Improved Algorithm

Apriori algorithm is the classical algorithm in data mining association rules. Because the Apriori algorithm needs scan database for many times, it runs too slowly. In order to improve the running efficiency, this paper improves the Apriori algorithm based on the Apriori analysis. The improved idea is that it transforms the transaction database into corresponding 0-1 matrix. Whose each vector and subsequent vector does inner product operation to receive support. And comparing with the given minsupport, the rows and columns will be deleted if vector are less than the minsupport, so as to reduce the size of the rating matrix, improve the running speeding. Because the improved algorithm only needs to scan the database once when running, therefore the running speeding is more quickly. The experiment also shows that this improved algorithm is efficient and feasible.

Application of Association Rules Based on CF Gene in Intrusion Detection

2014 ◽  
Vol 556-562 ◽  
pp. 2603-2606
Author(s):  
Rong Fu ◽  
Li Yan Liu ◽  
Ying Qian Zhang ◽  
Yi He
Keyword(s):  
Intrusion Detection ◽  
Association Rules ◽  
Association Rule ◽  
Detection Technology ◽  
Mining Association Rule ◽  
Improved Algorithm

By analyzing and studying the most current algorithms about mining association rule, the rules evaluated by minimum confidence could not ensure the validity of the rules and will generate unrelated rules which will affect the intrusion detection work. This paper proposes CF measure based on the previous work and applies the association rule algorithm based on CF to intrusion detection technology to detect the intrusion behaviors in the network. Finally, experiments show that improved algorithm is more efficient.

The Discussion on the Improved Association Rules Algorithm in Data Mining

2014 ◽  
Vol 989-994 ◽  
pp. 1985-1988
Author(s):  
Tao Wen ◽  
Li Sun ◽  
Li Zhu
Keyword(s):  
Data Mining ◽  
Pattern Matching ◽  
Association Rules ◽  
Potential Application ◽  
Huge Amount ◽  
Association Rules Mining ◽  
Classical Association ◽  
Improved Algorithm

The data mining can help to extract the information and knowledge with potential application value from a huge amount of data. In order to do the date mining scientifically and efficiently, this article provides an improved algorithm based on the classical association rules Aprior algorithm, taking advantage of which, we can engage in the association rules mining of the database to obtain the useful information. The improved algorithm avoids pattern matching and reduces the number of times of visiting the database, thus improves the speed of date mining to some extend.

Research and Application in Supply Chain Management Based on Correlation Analyze of Association Rules Algorithm

2006 ◽  
Vol 532-533 ◽  
pp. 1024-1027 ◽  
Author(s):  
Shou Ning Qu ◽  
Qin Wang ◽  
Kui Liu ◽  
De Jun Xu
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Association Rules ◽  
Association Rule ◽  
Chain Management ◽  
Improved Algorithm

In this paper, the association rule algorithm and its defects were analyzed. An improved algorithm was put forward for applying it to analysis the association of products fittings in SCM. The application of improved algorithm can mine which kinds of fittings or sets of items being matched to get a salable product or gain the higher profit. So it can not only instruct customer’s consumption but also can help the entrepreneur make a detailed and efficient internal plan.

scholarly journals A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jianfeng Xi ◽  
Zhenhai Gao ◽  
Shifeng Niu ◽  
Tongqiang Ding ◽  
Guobao Ning
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Road Traffic ◽  
Multilayered Structure ◽  
Test Model ◽  
Data Sampling ◽  
Accident Data ◽  
Improved Algorithm

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.

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