원문정보
초록
영어
With manufacturing technology developing persistently, hardware manufacturing cost becomes lower and lower. More and more computers equipped with multiple CPUs and enormous data disk emerge. Existing programming modes make people unable to make effective use of growing computational resources. Hence cloud computing appears. With the utilization of Map Reduce parallelized model, existing computing and storage capabilities are effectively integrated and powerful distributed computing ability is provided. Firstly, transform Apriori algorithm to Map Reduce model; realize Apriori parallel transformation; then use the way of compressing original transaction sets to improve the performance of Apriori algorithm in Hadoop framework; lastly, Map Reduce-Apriori algorithm is realized which is highly scalable for running in cloud computing environment.
목차
1. Introduction
2. Algorithm Analysis and Parallelization Transformation
3. Data Initialization
4. Iterative implementation
4.1 Calculate Frequent Itemsets at the kth layer
4.2. Calculate Candidate Itemsets at the (K+1) Th Layer
5. Generation of Association Rules
6. Experimental Analysis and Results
6.1. Experimental Data Set
6.2. Experimental Test Analysis
6.3. Analysis of Data Mining Results
7. Conclusion
References