We introduce a pattern extraction method which uses both the Lagrangian Multiplier and the Principal Component Analysis (PCA) to create patterns from raw sensory data. We have evaluated our method by applying a clustering method on constructed patterns. The results show that by using our proposed Lagrangian-based pattern extraction method, the existing clustering algorithms perform more accurately - by up to 20% higher compared with the state-of-the-art methods, especially in dealing with dynamic real-world data.
This repository is related to my paper: "Lagrangian-based Pattern Extraction for Edge Computing in the Internet of Things" The link to paper: http://epubs.surrey.ac.uk/851822/