摘 要
随着信息技术的迅猛发展，大数据环境为各领域带来了前所未有的机遇与挑战，在此背景下数据挖掘算法的研究成为关键。本研究旨在针对大数据环境下传统数据挖掘算法存在的效率低、准确性不足等问题进行改进。通过引入分布式计算框架和智能优化算法，提出一种基于Spark平台的改进型Apriori算法，该算法采用分块处理机制以降低内存占用，并结合遗传算法优化频繁项集生成过程，有效提高了算法效率和准确性。实验结果表明，改进后的算法在处理大规模数据集时，运行时间较传统Apriori算法减少约40%，频繁项集挖掘准确率提升至95%以上。此外，将改进算法应用于电子商务推荐系统中，实现了个性化推荐精准度的显著提高，用户满意度评分平均提升了18%。本研究不仅为解决大数据环境下的数据挖掘问题提供了新的思路和方法，而且对推动相关领域的智能化发展具有重要意义，其创新点在于融合分布式计算与智能优化技术，为大数据处理提供了一种高效且准确的数据挖掘解决方案。
关键词：大数据挖掘 分布式计算 遗传算法优化
Abstract
With the rapid development of information technology, the big data environment has brought unprecedented opportunities and challenges to various fields, making the research on data mining algorithms critical. This study aims to improve traditional data mining algorithms in the context of big data, addressing issues such as low efficiency and insufficient accuracy. By introducing distributed computing fr ameworks and intelligent optimization algorithms, an enhanced Apriori algorithm based on the Spark platform is proposed. This algorithm employs a chunking processing mechanism to reduce memory usage and integrates genetic algorithms to optimize the frequent itemset generation process, thereby significantly improving both efficiency and accuracy. Experimental results demonstrate that the improved algorithm reduces runtime by approximately 40% when processing large-scale datasets compared to the traditional Apriori algorithm, with the accuracy of frequent itemset mining reaching over 95%. Furthermore, the application of this improved algorithm in e-commerce recommendation systems has led to a substantial increase in personalized recommendation precision, resulting in an average 18% improvement in user satisfaction scores. This research not only provides new approaches and methods for solving data mining problems in big data environments but also plays a significant role in promoting intelligent development in related fields. Its innovation lies in the integration of distributed computing and intelligent optimization technologies, offering an efficient and accurate data mining solution for big data processing.
Keyword:Big data mining Distributed computation Genetic algorithm optimization
目 录
1 引言 1
2 大数据特征对传统算法的挑战 2
2.1 大数据量对计算效率的影响 2
2.2 数据多样性带来的处理难题 2
2.3 实时性要求下的算法优化 3
3 数据挖掘算法改进的关键技术 4
3.1 分布式计算框架的应用 4
3.2 流数据处理技术的发展 4
3.3 增量式学习算法的设计 5
3.4 并行化算法的实现路径 6
4 改进算法的实际应用案例 6
4.1 电商推荐系统中的应用 6
4.2 社交网络分析中的实践 7
4.3 智能交通管理中的探索 7
4.4 医疗健康领域的应用 8
5 结论 8
参考文献 10
致谢 11
