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Current context-aware adaptation techniques in smart environments are limited in their support for proactivity and user personalization. A reliance on developer modification and an inability to automatically learn from user interactions hinder their use for providing proactive services that can be adapted to the frequent changes of the context of individuals. To address these problems we propose a proactive and personalized approach to adaptation. Our approach integrates both Case-based Reasoning (CBR) and data mining techniques. It is based on CBR, but aided by data mining to extract user patterns and knowledge adaptation from users' interaction history.