Human activity recognition (HAR) from the time-series data generated by smarlt devices like smartphones and smartwathces is absolutely necessary for future intelligent health-care systems. A number of machine learning approaches have been devised for human activity recognition from such devices. However, most of the existing approaches have considered activity recognition as a supervised learning problem, which requires a training dataset for learning the activities. With ever-growing computing power of smart devices, the amount of data generated by these devises is also increasing in manifold. Thus, it becomes a tedious task to annotate the data collected from the smart devices. Being an unsupervised learning approach, cluster analysis provides meaningful insights on hidden patterns in the huge volume of data without training examples. Hence cluster analysis can be used as a preprocessing step in human activity recognition when there is no sufficient information about the number of activities. This paper presents a comparative study of three clustering algorithms such as K-means, hierarchical agglomerative clustering and Fuzzy C-means for human activity recognition problem. A method to automatically determine the number of activities is also demonstrated in this paper. Experimental results on two UCI activity recognition datasets show that FCM algorithm effectively categorizes the activities. © Springer Nature Switzerland AG 2020.