Transforming Food Dishes Data Columns for Effective K-Means Clustering

When working with datasets that include food dishes and their ingredients, the challenge arises of transforming such data into a format suitable for K-Means clustering. K-Means is a popular algorithm for clustering data into a specified number of groups. However, when the data involves lists of ingredients, traditional methods like Euclidean distance might not suffice. This article will guide you through the process of transforming a list of ingredients into vectors that can be effectively used with K-Means clustering, with a focus on normalization and the use of the Mahalanobis distance.

Introduction to K-Means Clustering

K-Means clustering is a method of unsupervised machine learning that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This method is widely used in various applications, including the analysis of food data, where understanding different types of dishes based on their ingredient composition can provide valuable insights.

Transformation of Ingredients Data

One of the food dishes in our dataset is represented as a list of ingredients. To make this data suitable for K-Means clustering, we need to convert it into a numerical format. Here is a step-by-step guide to achieve this:

Step 1: Normalized Histograms

The first step is to create a normalized histogram of ingredients per dish. This involves:

Creating a vector for each dish where the ith element represents the quantity of ingredient i, in grams. Normalizing the vector such that the total quantity is represented per kilogram or per gram of the dish.

For instance, if a dish contains:

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