I have a doubt whether after clustering using any algorithm is it possible to segment new data based on the learning from the previous data
2 Answers
The issue is that clustering algorithms are unsupervised learning algorithms. They don't need a dependent variable to predict classes. They are used to find structures/similarities in the data points. What you can do is, treat the clustered data as your supervised data.
The approach would be clustering and assigning labels in the train data. Treat it as a multi-class classification data, train a new multi-class classification model using your data and validate it on the test data.
Let train and test be the datasets.
clusters <- Clustering(train)
train[y] <- clusters
model <- Classification(train, train[y])
prediction <- model.predict(test)
However interestingly KMeans in sklearn provides fit and predict method. So using KMeans from sklearn you can predict in the new data. However, DBScan doesn't have predict which is quite obvious from it's working mechanism.
Clustering is an unsupervised mechanism where the number of clusters and the identity of the segments which need to be clustered are not known to the system.
Hence what you can do is to obtain the learning of a model which is trained for Clustering , classification,Identification or verification and apply that learning to your use case of clustering.
If the new data is from the same domain of the trained data most probably you will end up with better accuracy in clustering. (You need to properly choose the clustering methodology based on the type of data which you choose. eg for voice clustering Dominant sets and hierarchical clustering will be the most potential candidates). If the New data is from a different domain then the selected model may fail as it learned the features in correspond to your domain of training data.
Related