scholarly journals A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yang Xu ◽  
Mingming Zeng ◽  
Quanhui Liu ◽  
Xiaofeng Wang
Keyword(s):  
Genetic Algorithm ◽  
Big Data ◽  
Association Rules ◽  
Domain Knowledge ◽  
Computational Cost ◽  
Search Space ◽  
Association Mining ◽  
Association Rules Mining ◽  
Data Elements ◽  
Multilevel Association Rules

Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold.

scholarly journals ASSOCIATION RULES MINING IN BIG DATA

2018 ◽  
pp. 25-32 ◽  
Author(s):  
Nataliya Shakhovska ◽  
Roman Kaminskyy ◽  
Eugen Zasoba ◽  
Mykola Tsiutsiura
Keyword(s):  
Big Data ◽  
Association Rules ◽  
Distributed Databases ◽  
Data Sources ◽  
Data Types ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Data Definition ◽  
Subject Areas ◽  
Asymptotic Complexity

The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.

Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP

2021 ◽  
Vol 16 (1) ◽  
pp. 75-94
Author(s):  
Hairong Wang ◽  
Pan Huang ◽  
Xu Chen
Keyword(s):  
Association Rules ◽  
Real Data ◽  
Association Mining ◽  
Mining Method ◽  
Association Rules Mining ◽  
Factors Affecting ◽  
Intermediate Data ◽  
On Line ◽  
Analysis System ◽  
Mining Model

As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multi-dimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.

scholarly journals Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yang Ou ◽  
Zheng Jiang Liu ◽  
Hamid Reza Karimi ◽  
Ying Tian
Keyword(s):  
Genetic Algorithm ◽  
Resource Management ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Immune Genetic Algorithm ◽  
Coding Scheme ◽  
The Relationship ◽  
Multilevel Association Rules

This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

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