A Synthesized Data Mining Algorithm Based on Clustering and Decision Tree

With the development of information technology and computer science, high-capacity data appear in our lives. In order to help people analyzing and digging out useful information, the generation and application of data mining technology seem so significance. Clustering and decision tree are the mostly used methods of data mining. Clustering can be used for describing and decision tree can be applied to analyzing. After combining these two methods effectively, we can reflect data characters and potential rules syllabify. This paper presents a new synthesized data mining algorithm named CA which improves the original methods of CURE and C4.5. CA introduces principle component analysis (PCA), grid partition and parallel processing which can achieve feature reduction and scale reduction for large-scale datasets. This paper applies CA algorithm to maize seed breeding and the results of experiments show that our approach is better than original methods.