In designing autonomous service systems such as assistive robots for the aged and the disabled, discovery and prediction of human actions are important and often crucial. Patterns of human behavior, however, involve ambiguity, uncertainty, complexity, and inconsistency caused by physical, logical, and emotional factors, and thus their modeling and recognition are known to be difficult. In this paper, a non-supervised learning framework of human behavior patterns is suggested in consideration of human behavioral characteristics. Our approach consists of two steps. In the first step, a meaningful structure of data is discovered by using Agglomerative Iterative Bayesian Fuzzy Clustering (AIBFC) with a newlyproposed cluster validity index. In the second step, the sequence of actions is learned on the basis of the structure discovered in the first step and by utilizing the proposed Fuzzy-state Q-learning (FSQL) process. These two learning steps are incorporated in an amalgamated framework, AIBFC-FSQL, which is capable of learning human behavior patterns in a non-supervised manner and predicting subsequent human actions. Through a number of simulations with typical benchmark datasets, we show that the proposed learning method outperforms several well-known methods. We further conduct experiments with two challenging real-world databases to demonstrate its usefulness from a practical perspective.