원문정보
초록
영어
In recent years, the amount of data into a geometric growth puts forward higher requirements on data mining algorithm. In the process of frequent itemsets of traditional Apriori algorithm produced, frequent itemsets' generation and storage are quite a waste of time and space. In this paper, we put forward a new Hash table and use the technology to improve the algorithm and get SamplingHT algorithm, through a lot of contrast experiments showed that the new algorithm enhances performance when frequent itemset is generated, and effectively reduce the database scan times, In order to achieve more optima.
목차
Abstract
1. Introduction
2. Association Rule Data Mining Technology
2.1 Basic concept of Data Mining
2.2 Association Rule Mining Algorithm
3. SamplingHT Algorithm
3.1 The Main Steps of SamplingHT Algorithm
3.2 New Hash Function
3.3 SamplingHT Code:
4. Experiments and Analysis
4.1 Experiment 1
4.2 Experiment 2
4.3 Experiment 3
5. Conclusion
Acknowledgements
References
1. Introduction
2. Association Rule Data Mining Technology
2.1 Basic concept of Data Mining
2.2 Association Rule Mining Algorithm
3. SamplingHT Algorithm
3.1 The Main Steps of SamplingHT Algorithm
3.2 New Hash Function
3.3 SamplingHT Code:
4. Experiments and Analysis
4.1 Experiment 1
4.2 Experiment 2
4.3 Experiment 3
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
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