Journal of Data Mining in Genomics & Proteomics

Improving similarity search in irregular time-series using dynamic time warping

2^nd International Conference on Big Data Analysis and Data Mining

November 30-December 01, 2015 San Antonio, USA

Arvind Pandiyan, Varun V Shenoy, Poojitha Karlapalem and Dinkar Sitaram

PES Institute of Technology, India

Posters-Accepted Abstracts: J Data Mining In Genomics & Proteomics

Abstract:

Dynamic time warping (DTW) is one of the prevailing distance measures used in time-series, though it is computationally costly. DTW is providing optimal alignment between two time-series. The time-series show similarity and DTW exploits the existence of similarity. In this paper, we present techniques that can be employed to improve similarity search in irregular time series data. The drawbacks in the classical approach of converting the irregular time series to a regular one before the similarity search techniques are identified and appropriate solutions for overcoming them are implemented. Simulations with real and synthetic data sets reveal that the proposed techniques are performing well with irregular time series data sets.

Biography :

Arvind Pandiyan is currently pursuing his MS in Computer Science in UT, Dallas and has graduated from PES Institute of Technology. He has research interests in data mining, machine learning and big data analysis.

Email: arvindpandiyan@gmail.com