Hierarchical Clustering Method for Extraction of Knowledge from a Large Amount of Data

Nishizawa, Hidekazu; Obi, Takashi; Yamaguchi, Masahiro; Ohyama, Nagaaki

doi:10.1007/s10043-999-0302-2

Cited by 4 publications

(1 citation statement)

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“…HC has been extensively used in data analysis and signal processing due to its simplicity and visual interpretation of the hierarchy structure, [25][26][27] and is considered here as a means of optimizing PAA segment frame sizes according to sequencesample similarity. HC is used to de¯ne optimal PAA frame size according to hierarchical distance measures, but at the same time not to compromise the original PAA's simplicity for further implementations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Hierarchical Clustering — Piecewise Aggregate Approximation, with Applications

Gallimore

Bingham

Chen

et al. 2016

Int. J. Comp. Intel. Appl.

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PublishedPiecewise Aggregate Approximation (PAA) provides a powerful yet computationally e±cient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and well-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insu±ciency or over-segmentation, and because of this, additional relatively complex modi¯cations 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 e±cacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies on non-timeseries data viz. fault detection on industrial gas turbines and ultrasonic biometric face identi¯-cation. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional bene¯ts with regard to both cluster separation and classi¯cation performance, compared to traditional PAA and the APCA alternative. The method is therefore demonstrated to provide a robust readily implemented algorithm for rapid FE and identi¯cation for datasets.

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Section: Introductionmentioning

confidence: 99%