Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.57
Tomoki Watanuma , Kobe University
Tomonobu Ozaki , Kobe University
Takenao Ohkawa , Kobe University
Since most structured data mining techniques specialize in mining from single structured data, it cannot handle more realistic data which consist of different and plural kinds of structured data. To cope with this problem, we propose an algorithm for constructing decision trees from multidimensional structured data by introducing the techniques for mining correlated and closed patterns with effective pruning capabilities into the traditional TDIDT approach. The results of the experiments with real world data show the effectiveness of the proposed algorithm.
T. Ohkawa, T. Watanuma and T. Ozaki, "Decision Tree Construction from Multidimensional Structured Data," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 237-241.