Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Piecewise Aggregate Approximation (PAA) provides a porful yet computationally efficient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and ll-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies viz. fault detection on industrial gas turbines and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and APCA alternatives. The method is therefore demonstrated to provide a robust and readily implemented algorithm for rapid FE and identification for datasets.
['Yu Zhang', 'Michael Gallimore', 'Chris Bingham', 'Jun Chen', 'Yong Xu']
I am writing a research paper on a idea Hybrid Hierarchical Clustering — Piecewise Aggregate Approximation, with Applications
[{"from": "human", "value": "I am writing a research paper on a idea Hybrid Hierarchical Clustering \u2014 Piecewise Aggregate Approximation, with Applications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Piecewise Aggregate Approximation (PAA) provides a porful yet computationally efficient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and ll-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insufficiency or over-segmentation, and because of this, additional relatively complex modifications have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The efficacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies viz. fault detection on industrial gas turbines and ultrasonic biometric face identification. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional benefits with regard to both cluster separation and classification performance, compared to traditional PAA and APCA alternatives. The method is therefore demonstrated to provide a robust and readily implemented algorithm for rapid FE and identification for datasets."}]