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

fYear

2011

fDate

5-7 Dec. 2011

Firstpage

375

Lastpage

380

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia (ISM), 2011 IEEE International Symposium on

Conference_Location

Dana Point CA

Print_ISBN

978-1-4577-2015-4

Type

conf

DOI

10.1109/ISM.2011.68

Filename

6123375