In order to deal with the problem of over-coarse or over-fine knowledge granularity in data mining

an attribute induction algorithm based on quantized concept lattice is proposed by using the partial property of the concept lattice. Firstly

the quantized concept lattice is defined by quantifying concept extension of the concept lattice

and then it is generalized using concept ascension according to the Hasse diagram of the concept lattice so as to get the induction with multi-level and multi-attribute based on the quantized concept lattice. Compared with the attribute-oriented induction(AOI)algorithm

the proposed algorithm can not only perform the unitary induction of AOI

but also carry out the induction with multi-level and multi-attribute

and the path of attribute generalization is not unique. Moreover

it is easy to find proper generalized paths and thresholds in Hasse diagram of quantized concept lattice to obtain the reasonable results required by users.