Identification of similarities and clusters of bread baking recipes based on data of ingredients
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Stefan Anlauf
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Sebastian Dorl
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Theresa Hirz
Abstract
We define the similarity of bakery recipes using different distance calculations and identify groups of similar recipes using different clustering algorithms. Our analyses are based on the relative amounts of ingredients included in the recipes. We compare different clustering algorithms (k-means, k-medoid, and hierarchical clustering) to find the optimal number of clusters. Besides the standard distance calculation (euclidean distance), we test three other distance metrics (hamming distance, manhattan distance, and cosine similarity). Additionally, we reduce the impact of raw materials used in large quantities by applying two different data transformations, namely the logarithm of the original data and the binarization of the original data. Clustering recipes based on their ingredients can improve the search for similar recipes and therefore help with the time-consuming process of developing new recipes. Using the hierarchical clustering on the logarithm of the original data, we can separate 704 recipes into three different clusters, achieving a Silhouette Score of 0.531. We visualize our results via dendrograms representing the recipes’ hierarchical separation into individual groups and sub-groups.
Funding source: Österreichische Forschungsförderungsgesellschaft
Award Identifier / Grant number: INTEGRATE – 892418
Funding source: The research reported in this paper has been funded by BMK, BMDW, and the State of Upper Austria in the frame of the COMET Programme managed by FFG
Acknowledgment
The project is a cooperation between University of Applied Sciences Upper Austria, Software Competence Center Hagenberg and backaldrin International The Kornspitz Company GmbH. The data was provided by the company backaldrin International The Kornspitz Company GmbH.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: The research reported in this paper has been funded by BMK, BMDW, and the State of Upper Austria in the frame of the COMET Programme managed by FFG. This research was also funded by Österreichische Forschungsförderungsgesellschaft (INTEGRATE – 892418).
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Data availability: Not applicable.
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