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Advanced Signal Processing Techniques for Feature Extraction in Data Mining

by Maya Nayak, Bhawani Sankar Panigrahi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 9
Year of Publication: 2011
Authors: Maya Nayak, Bhawani Sankar Panigrahi
10.5120/2387-3160

Maya Nayak, Bhawani Sankar Panigrahi . Advanced Signal Processing Techniques for Feature Extraction in Data Mining. International Journal of Computer Applications. 19, 9 ( April 2011), 30-37. DOI=10.5120/2387-3160

@article{ 10.5120/2387-3160,
author = { Maya Nayak, Bhawani Sankar Panigrahi },
title = { Advanced Signal Processing Techniques for Feature Extraction in Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 9 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume19/number9/2387-3160/ },
doi = { 10.5120/2387-3160 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:06:33.609118+05:30
%A Maya Nayak
%A Bhawani Sankar Panigrahi
%T Advanced Signal Processing Techniques for Feature Extraction in Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 9
%P 30-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper gives a description of various signal processing techniques that are in use for processing time series databases for extracting relevant features for pattern recognition. In addition to describe the normally used signal processing methods, we also present a novel signal processing technique, which is a modification of the well-known Short-Time Fourier Transform (STFT) and Wavelet Transform for extracting suitable features for both visual and automatic classification of non-stationary time-series databases [3][5]. Although the new signal processing technique is useful for speech, medical, financial, and other types of time varying databases, only the power network disturbance time series is used for computing features for visual and automatic recognition.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction Short Time Fourier Transform (STFT) Wavelet Transform S-transform Transient Swell Sag

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