An algorithm to building a fuzzy decision tree
for data classification problem based on the
fuzziness intervals matching

Abstract. Nowaday, on demand to reflect the real world, so we have many imprecise stored business data warehouses. The precise data classification cannot solve all the requirements. Thus, fuzzy decision tree classification problem have role is important of fuzzy data mining problem. The fuzzy decision classification based on fuzzy set theory have some limitations derived from the inner selves of it. The hedge algebra with many advantages has become a really useful tool for solving the fuzzy decision tree classification. However, sample data homogenise process based on quantitative methods of the hedge algebra with some restrictions remain appear because of error in the process and not the result tree truly versatile. So, the fuzzy decision tree obtained not always have high predictable. In this paper, we using fuzziness intervals matching an approach hedge algebra, we proposed the inductive learning method HAC4.5 fuzzy decision tree to obtain the fuzzy decision tree with high predictable.