CAJ | 학술논문

　　针对K-means算法处理海量数据存在严重的内存不足，提出利用MapReduce并行化K-means，但是普通的K均值存在收敛速度慢、易陷入局部最优和对初始聚类中心的选取等局限性，因此选择了经ACO改进过的ACO-K-means聚类算法。实验结果表明，经MapReduce并行化的ACO-K-means，不仅具有良好的加速比和扩展性，其收敛性以及聚类精度均得到了改善。
　　침대K-means산법처리해량수거존재엄중적내존불족，제출이용MapReduce병행화K-means，단시보통적K균치존재수렴속도만、역함입국부최우화대초시취류중심적선취등국한성，인차선택료경ACO개진과적ACO-K-means취류산법。실험결과표명，경MapReduce병행화적ACO-K-means，불부구유량호적가속비화확전성，기수렴성이급취류정도균득도료개선。
There is a serious lack of memory when use K-means to deal with massive data. In this paper MapReduce is used to parallelize K-means. Due to ordinary K-means has slow rate of convergence, easily fall into local optimization and the limita-tions of selection of initial cluster centers, Ant Colony Optimization(ACO) is led into K-means. The result demonstrates ACO-K-means clustering algorithm based on MapReduce model has high speedup and good scalability, and it’s convergence and clustering accuracy are also improved in some degree.