發(fā)布時間：2018-04-22 13:01

  本文選題：K均值 + 檢索結果聚類　； 參考：《計算機工程與設計》2017年04期

【摘要】：針對搜索引擎檢索大規(guī)模數(shù)據(jù)時結果聚類的性能有限問題,提出一種查詢日志輔助的改進K-Means算法。將傳統(tǒng)的K-Means聚類擴展為多層次聚類的形式,實現(xiàn)檢索對象與檢索結果之間的聚類;通過引入檢索日志,輔助提升聚類的效果,實現(xiàn)檢索結果推送的高相關性。實現(xiàn)結果表明,基于該算法的檢索結果聚類,有著較高的準確率,檢索過程的時間開銷較低,綜合效率與準確率而言,該算法是一種理想的檢索結果聚類方法。
[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.
【作者單位】： 江西科技師范大學黨委組織部;南昌理工學院經濟管理學院;
【分類號】：TP391.3
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本文編號：1787319