基于Web的自映射空间决策树方法研究

Decision-tree Induction from Self-map Space Based on Web

论文讨论了Web信息的自映射空间模型和决策树算法的实现。从应用角度提出一种新的决策树方法SMS-DT,并根据映射序列的不同在内节点得到唯一的映射属性值。在关系和属性信息的基础上,自映射由不同数据集选择合理的空间模型,得到有效的决策树映射方法。实验结果进一步证实自映射决策树具有全面性与精确性。由于自映射决策树较好地软化了数量属性论域的划分边界,从而为进一步满足Web信息检索提供了一种个性化的高效信息检索工具。

This paper proposes an improved decision tree method for Web information retrieval with self-map attributes.The self-map tree has a value of self-map attribute in its internal node based on dissimilarity between a pair of map sequences.The method selects self-map which exists between data by exhaustive search based on relation and attribute information.Experimental results confirm that the improved method constructs comprehensive and accurate decision tree.Moreover,an example shows that the self-map decision tree is promising for data mining and knowledge discovery.