I have a dataset in which it contains both numerical and categorical data. This can be done using supervised learning algorithms, but I am eager to see how this data can be clustered using some unsupervised learning algorithm (K-Means clustering algorithm is currently used).
For example, the Gender, None, Low, Medium, High, Breakfast, Lunch, and Dinner columns are represented in binaries. eg: Gender represents 1 for male and 0 for female. While the rest of the above-mentioned columns represents 0 for unavailability and 1 for availability. The Meal and the Exercise columns are also categorical but are not binary. For example, in the Meal, if it is breakfast then 1, lunch = 2, and dinner = 3. So how can we use this type of mixed dataset for clustering? Please ignore the Event column since it is the target column.
Further, should we need to normalize the rest of the numerical data before adding to any sort of unsupervised learning algorithm? And how can we deal with those different types of categorical data? Your guidance is greatly appreciated.