Title :
Temporal Dietary Patterns Using Kernel k-Means Clustering
Author :
Khanna, Nitin ; Eicher-Miller, Heather A. ; Boushey, Carol J. ; Gelfand, Saul B. ; Delp, Edward J.
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
Abstract :
Chronic diseases, such as heart disease, diabetes, and obesity, have been linked with diet. Nutrient intake is also associated with diet. However, much of the research completed to elucidate these associations has not incorporated the concept of time. This paper introduces the concept of temporal dietary patterns and demonstrates a novel construct of 24-hour temporal dietary patterns for energy intake, present in a sample of the adult U.S. population 20 years and older (NHANES 1999-2004 dataset). An appropriate distance metric is proposed for comparing 24-hour diet records and is used with kernel k-means clustering to identify the temporal dietary patterns.
Keywords :
biology computing; diseases; epidemics; pattern clustering; chronic disease; diet record; distance metric; heart disease; kernel k-means clustering; temporal dietary pattern; Databases; Educational institutions; Euclidean distance; Kernel; Quantization; Vectors; Kernel k-Means; NHANES; Temporal Dietary Patterns; diet patterns;
Conference_Titel :
Multimedia (ISM), 2011 IEEE International Symposium on
Conference_Location :
Dana Point CA
Print_ISBN :
978-1-4577-2015-4
DOI :
10.1109/ISM.2011.68