Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements. Machine learning offers a promising approach to building sleep/wake state detectors from actigraphy data. However, current machine learning approaches rely on expert labeled training data that can be expensive and laborious to acquire. In this work, we introduce a novel approach for integrating unsupervised learning algorithms and domain knowledge heuristics, based on statistical properties of clustered sleep and wake epochs, to develop reliable sleep/wake state prediction models using unlabeled wrist actigraphy data. Experimental results using a dataset of 37 participants and covering 282 sleeping periods demonstrate the viability of the proposed approach on developing sleep/wake state detection models from unlabeled actigraphy data with a predictive performance that is comparable with the performance of models developed using some state-of-the-art supervised learning algorithms applied to labeled actigraphy data. Our results lay the groundwork for developing fully automated machine learning models for sleep/wake state prediction and sleep parameters estimations by eliminating the need for costly and labor-intensive expert annotations of PSG recordings for labeling actigraphy data.