Incremental density clustering framework based on dynamic microlocal clusters

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors' streaming data analysis.