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Human activity recognition (HAR) has a wide application in daily life. With wearable sensors, people's activity can be monitored, recorded and analysed. However, most existing methods did not make full use of human activity data and their features. In this paper, a new method based on feature selection and clustering algorithm is proposed. We established two-layer classification models, respectively, in the ankle, the chest, the wrist and the mixed accelerometer data. K-means clustering algorithm was first used to obtain a broad classification of the activities and then we conducted two rounds of classification, among which feature selection was performed in each layer. A significance analysis was also carried out in the final experiment and we compared the performance of the final model from the mixed accelerometer data with other algorithms, the results showed that the recognition performance of our model was significantly better, and the average F 1 score was as high as 0.969 in publicly available PAMAP2 dataset. Compared with other methods, our model achieved the highest recognition rate. The method proposed in this paper can greatly improve the recognition rate of human activity and effectively evaluate daily activity. [ABSTRACT FROM AUTHOR]

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm++, BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies. [ABSTRACT FROM AUTHOR]