Signature based mining framework for event sequences and its applications in healthcare data using language model.

D. Vetrithangam; N. Uma Maheshwari; R. Venkatesh

No Detail Found!

Signature based mining framework for event sequences and its applications in healthcare data using language model.

Author(s)

D. Vetrithangam Department of Computer Science and Engineering, RVS College of Engineering and Technology, Dindigul, Tamilnadu, India

N. Uma Maheshwari Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

R. Venkatesh Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

Abstract

Temporal event signature mining for knowledge discovery is a difficult problem. In this paper a framework is designed to know a temporal knowledge about the large scales signature mining of longitudinal heterogeneous event data. This framework mainly deals with the mining of high order latent event structure and its relationship within single and multiple event sequences. Here, the heterogeneous event sequences maps to geometric image by encoding structured into spatial temporal shape process. A Probabilistic language modeling is used to extract high order events from large scale data. Also, presents a doubly constrained conventional sparse coding to learn interpretable and shift invariant latent temporal event signature. This can be shown by the sparsity in the data and latent factor model on the _-divergence. An optimization scheme is also used to perform large scale incremental learning of group specific temporal event signatures.

Publication Details

Page(s) 167-171

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 59, Issue: 1, Year: 2014

Request the full text from PASTIC

Citations

Downloads

Views