QLA-Means:检索结果聚类方法

发布时间：2018-04-22 13:01

  本文选题：K均值 + 检索结果聚类　； 参考：《计算机工程与设计》2017年04期

【摘要】：针对搜索引擎检索大规模数据时结果聚类的性能有限问题,提出一种查询日志辅助的改进K-Means算法。将传统的K-Means聚类扩展为多层次聚类的形式,实现检索对象与检索结果之间的聚类;通过引入检索日志,辅助提升聚类的效果,实现检索结果推送的高相关性。实现结果表明,基于该算法的检索结果聚类,有着较高的准确率,检索过程的时间开销较低,综合效率与准确率而言,该算法是一种理想的检索结果聚类方法。
[Abstract]:Aiming at the limited performance of result clustering when searching large scale data by search engine, an improved K-Means algorithm with query log assistance is proposed. The traditional K-Means clustering is extended to multi-level clustering to realize the clustering between the retrieval objects and the retrieval results. The retrieval log is introduced to help improve the clustering effect and to achieve the high correlation of the retrieval results push. The results show that the algorithm has high accuracy and low time cost. The algorithm is an ideal clustering method for retrieval results in terms of both efficiency and accuracy.
【作者单位】： 江西科技师范大学党委组织部;南昌理工学院经济管理学院;
【分类号】：TP391.3
，

本文编号：1787319