Cuckoo search algorithm for clustering food offers

Author

Chifu, Viorica R. ; Salomie, Ioan ; Chifu, Emil St ; Izabella, Balla ; Pop, Cristina Bianca ; Antal, Marcel

Author_Institution

Dept. of Comput. Sci., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania

fYear

2014

fDate

4-6 Sept. 2014

Firstpage

17

Lastpage

22

Abstract

This paper presents a method for clustering food offers based on the cuckoo search algorithm. The proposed method clusters food offers based on the similarity between their nutritional features (e.g. calcium, vitamins etc.) and/or ingredients. The similarity is evaluated by using the Sorensen-Dice coefficient. To test the clustering method proposed here, we have developed in-house a set of 800 food offers. The food offers have been generated as starting from a set of food recipes (provided in an XML standard for sharing recipes) and a database containing information about nutritional features. This database stores the nutritional features of each food type, as provided by the Agricultural Research Service of the United States Department of Agriculture. We evaluated the performance of our clustering method by using the following metrics: the Dunn Index, the Davies-Bouldin index, and the Average Item-Cluster Similarity.

Keywords

XML; pattern clustering; search problems; Davies-Bouldin index; Dunn index; Sorensen-Dice coefficient; XML standard; average item-cluster similarity; cuckoo search algorithm; food offer clustering; similarity evaluation; Calcium; Clustering algorithms; Clustering methods; Heuristic algorithms; Indexes; Measurement; cuckoo search; data clustering; food offers;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computer Communication and Processing (ICCP), 2014 IEEE International Conference on

Conference_Location

Cluj Napoca

Print_ISBN

978-1-4799-6568-7

Type

conf

DOI

10.1109/ICCP.2014.6936974

Filename

6936974