Paper Info
Title
An association rule analysis framework for complex physiological and genetic data
Abstract
Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set ui (e.g. u1 numerical data streams, u2 images, u3 videos, u4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases.
Year
DOI
Venue
2012
10.1007/978-3-642-29361-0_17
HIS
Keywords
Field
DocType
complex disease,multidimensional association rule mining,genetic information,mmarcd incrementally,complex physiological,u1 numerical data stream,complex data,change detection,entire system,association rule analysis framework,genetic data
Data mining,Data stream mining,Disease,Change detection,Multivariate statistics,Computer science,Complex data type,Correlation,Association rule learning,Artificial intelligence,Hidden variable theory,Machine learning
Conference
Citations 
PageRank 
References 
4
0.43
17
Authors
8
Name
Order
Citations
PageRank
Jing He136248.04
Yanchun Zhang23059284.90
Guangyan Huang341942.85
Yefei Xin470.86
Xiaohui Liu55042269.99
Hao Lan Zhang63510.99
Stanley Chiang740.76
Hailun Zhang840.76