With the surge in data production, Machine Learning techniques are now commonly used to build intelligent models. Traditionally, powerful platforms process data collected from endpoint devices. However, to address security threats and minimize communication traffic, models can be learned near endpoint devices, despite their resource shortage. K-means clustering is among the most common machine learning tasks used for embedded applications. Because the system is running on scarce resources, the learning process needs to obey a certain time limit. Even if current implementations of K-means have been optimized for embedded devices, they do not consider running within a predefined time budget. In this paper, we propose a deadline-aware and energy-efficient version of K-means called Embedded K-means (EK-means) 1 , that relies on two main ideas : (1) smartly select the right subset of data to train on to meet the deadline at the expense of the smallest clustering error possible ; (2) by dropping part of the data, slack times are identified and exploited opportunistically to apply Dynamic Voltage and Frequency Scaling techniques (DVFS) so as to decrease the energy consumption of the learning task. EK-means has been built on top of an I/O optimized version of K-means for embedded devices to maintain a low I/O proportion regardless of memory constraints. EK-means allows to cluster data while meeting more than 98% of the deadlines with a loss of 1.43% of clustering quality, and an energy reduction of up to 84.26%.