Accurate and timely detection of estrus in cows is very important for reproduction, health, and milk production. Traditional estrus detection methods like manual observation and chin head chalking are outdated and not suitable for the dairy farm because of large number of animals. A lot of automated estrus detection methods have been proposed such as milk yield fluctuation, milk progesterone detection. However, these methods are either too complex to implement or have low detection rate. Therefore, this study proposed a novel estrus detection method that can be implemented easily with enhanced detection rate. This method extracted features from 3D acceleration data, collection from accelerometer attached to cow's neck. The collected data was clustered using k-means into 3 clusters. Categories were assigned based on data variance. As a result, the three clusters were categorized as: low activity, medium activity, or high activity. Based on this information, activity index was calculated and then it was used for the estrus detection. Two machine learning classifiers namely SVM, and D-trees were employed for the activity recognition. SVM and D-tree achieved an accuracy of 96% and 86% respectively.