CAJ | 학술논문

采用传统算法进行混合型数据库查询，由于文档与查询的匹配过于严格，存在对检索词的重要程度无法区分的问题，造成无法进行检索结果的排序的缺陷。为此，提出了一种基于改进均值聚类算法的混合型数据库查询优化方法。建立混合型数据库查询执行计划代价模型，精准的定义了数据库查询执行代价。将改进均值聚类算法与粒子群算法有效的相融合，将粒子群中的粒子划分为多个子群，更新所有的粒子，通过粒子间的相互信息融汇，搜寻到数据库查询最优解。实验仿真证明，基于改进均值聚类算法的混合型数据库查询优化方法精确度高，效率高。
채용전통산법진행혼합형수거고사순，유우문당여사순적필배과우엄격，존재대검색사적중요정도무법구분적문제，조성무법진행검색결과적배서적결함。위차，제출료일충기우개진균치취류산법적혼합형수거고사순우화방법。건립혼합형수거고사순집행계화대개모형，정준적정의료수거고사순집행대개。장개진균치취류산법여입자군산법유효적상융합，장입자군중적입자화분위다개자군，경신소유적입자，통과입자간적상호신식융회，수심도수거고사순최우해。실험방진증명，기우개진균치취류산법적혼합형수거고사순우화방법정학도고，효솔고。
The traditional algorithm hybrid database query, due to the documents and queries match is too strict, the exis?tence of retrieval words are indistinguishable to the importance of the problem, cause could not be the sort of results. To this end, this paper proposes a hybrid database query based on improved average clustering algorithm optimization method. To create a hybrid database query execution plan cost model, accurate defines the database query execution cost. Will improve average clustering algorithm and particle swarm optimization (pso) algorithm combines effectively, particles in the particle swarm multiple sub divided into groups, update all the particles, through the integration of mutual information between par?ticles, search the database searching optimal solution. Experimental simulation show that the proposed based on improved average clustering algorithm of hybrid database query optimization method is of high precision and high efficiency.