Could you please help me to understand it because I'm not sure if I got it correctly.Let's say I have a dataset, of persons, with 100 features, various characteristics like height, weight, age, etc. I want to classify if are normal or abnormal. By abnormal I mean if a 20 years old man is 170cm and 150kg to identify it as abnormal.Should I use Self Organising Map to reduce the dimensionality (these 100 features) and then K-means to classify them into normal and abnormal? Is that a correct approach? Or I can use just K-NN to classify them into normal - abnormal without any dimensionality reduction?How many features can I use with K-NN? All the examples I've found so far use just two.And if later I'd like to find why this person has included into the abnormal class how can I find that this happened because of these two features, his weight according to his height?
Deciding about dimensionality reduction, classification and clustering?
machine learning;classification;clustering;dimensionality reduction