find anomalies in records of categorical data
Take a look on nearest neighborhoods method and cluster analysis. Metric can be simple (like squared error) or even custom (with predefined weights for each category).
Nearest neighborhoods will answer the question 'how different is the current row from the other row' and cluster analysis will answer the question 'is it outlier or not'. Also some visualization may help (T-SNE).
I have a dataset with m observations and p categorical variables (nominal), each variable X1,X2...Xp has several different classes (possible values). Ultimately I am looking for a way to find anomalies i.e to identify rows for which the combination of values seems incorrect with respect to the data I saw so far. So far I was thinking about building a model to predict the value for each column and then build some metric to evaluate how different the actual row is from the predicted row. I would greatly appreciate any help!