Data Mining in Analysis of Biomechanical Signals

2009 ◽  
Vol 147-149 ◽  
pp. 588-593 ◽  
Author(s):  
Marcin Derlatka ◽  
Jolanta Pauk
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Cerebral Palsy ◽  
Spina Bifida ◽  
Decision Tree ◽  
Principal Component ◽  
Data Preprocessing ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

In the paper the procedure of processing biomechanical data has been proposed. It consists of selecting proper noiseless data, preprocessing data by means of model’s identification and Kernel Principal Component Analysis and next classification using decision tree. The obtained results of classification into groups (normal and two selected pathology of gait: Spina Bifida and Cerebral Palsy) were very good.

scholarly journals Penerapan Principal Component Analysis untuk Peningkatan Kinerja Algoritma Decision Tree pada Iris Dataset

2020 ◽  
Vol 1 (2) ◽  
pp. 55-58
Author(s):  
Putri Kurnia Handayani
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Decision Tree ◽  
Supervised Learning ◽  
Principal Component ◽  
Component Analysis

Data mining merupakan salah bidang ilmu yang bermanfaat untuk pengenalan pola/knowledge yang tersimpan dalam database. Klasifikasi merupakan salah satu peran dalam bidang data mining. Termasuk ke dalam supervised learning, klasifikasi digunakan untuk memprediksi objek yang belum memiliki kelas/label. Penggunaan algoritma decision tree untuk proses mining dataset bunga iris dikarenakan kemudahan dalam representasi knowledge yang dihasilkan. Selain itu, decision tree juga termasuk ke dalam eager learner sehingga akurasi dari knowledge yang dihasilkan lebih baik. Penggunaan principal component analysis (PCA) dalam optimasi algoritma decision tree, dilakukan saat preprocessing dataset. PCA berfungsi untuk mereduksi dimensi, fitur yang saling berkorelasi akan dipertahankan. Penggunaan dataset publik bunga iris diambil dari UCI Repository. Berdasarkan hasil perhitungan, akurasi algoritma decision tree setelah dilakukan optimasi dengan PCA terhadap dataset bunga iris sebesar 95.33%.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

Sign in / Sign up

Export Citation Format

Share Document