植入引导式层次聚类Mashup服务推荐算法*

针对传统Mashup服务推荐算法在关键字聚合搜索和网络构建等方式中计算复杂度过高的问题,提出一种基于语义标签的植入引导式层次聚类Mashup服务推荐算法。首先,为提高聚类算法的收敛精度,提高算法运行效率来满足大型数据搜索对算法简化的需求,采用数据预处理和植入易于获取具有代表性的样本数据对聚类进行引导,防止层次聚类算法顶层集分类失败导致的算法聚类失败。其次,利用改进的聚类算法结合实际的Mashup服务数据库,设计了植入引导式层次聚类Mashup服务推荐算法。最后,通过通过仿真对比表明,基于语义的植入式半监督层次聚类Mashup服务推荐算法的精度要好于对比算法,验证所提算法的有效性。

Mashup service recommendation algorithm based on Implantable guided hierarchical clustering algorithm

The traditional Mashup service recommendation is aggregation algorithm based on Keywords, the network construction cost and complexity of computation method are very high, in order to solve this problem, present the Mashup service recommendation algorithm based on Implantable semi supervised hierarchical clustering algorithm. Firstly, use the climbing tools to collect ProgrammableWeb Mashup service information, and use the suffix stripping algorithm to modify the Mashup service label with noun form, as the research and analysis data sets. Secondly, in order to improve the accuracy of clustering algorithm, proposed an implantable semi supervised hierarchical clustering algorithm, by implantation the the core set approach of hierarchical clustering algorithm, to prevent the hierarchical clustering algorithm top set classification failure and effect of Mashup service recommendation algorithm accuracy. The simulation results show that, the recommendation accuracy of Implantable semi supervised hierarchical clustering algorithm is better than comparison algorithm in Mashup services recommendation, that verify the effectiveness of the proposed algorithm.