| 摘要: |
| 用户行为分析是Web站点信息推荐中的重要方法,被广泛应用在该领域的诸多算法中.PageGather算法是其中有代表性的一种.旨在解决静态PageGather算法输入数据量过大、时间复杂度高的问题,使其更具实用性.通过引入渐进学习和分布的机制,给出了改进的算法PG+和PG++,并进行了实验分析.改进后,既保证了算法的等效性,又明显提高了效率. |
| 关键词: Web 渐进 分布式 PageGather 聚类 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Incremental and Distributed Web Page Clustering Algorithms PG+ and PG++ |
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WANG Qi-xin,LI Yi,DONG Li,NIE Yu,WANG Ke-hong
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| Abstract: |
| A user behavior analysis is an important approach in many algorithms for the Web site information recommendation, among which, PageGather is a typical algorithm. However, the original PageGather algorithm is static, which needs too many data inputs and too much computing time. In this paper, incremental learning and distributed computation mechanisms are introduced into PageGather, so that two improved algorithms PG+ and PG++ are proposed. At the same time, corresponding experimental results are presented and analyzed.The improved algorithms are equivalent to the static PageGather algorithms.And better effect has got. |
| Key words: Web incremental distributed PageGather clustering |
