Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Sign up to join this community
I have some 1 dimensional data. Each record in the data is a specific time of the day. In order to cluster it I projected the data onto a circle of radius 1 unit. Now I need to find clusters in this data. The number of clusters are unknown and it is preferred to find clusters with high density of records in them. By density I mean that a large volume of records should be packed in a small space.
How should I go about finding clusters in the above mentioned data?
Instead of projecting into the circle and thus making your problem 2d, why don't you just use a cyclic distance measure?
This problem should be straightforward by doing kernel density estimation on the (cyclic) time of day. Then find the peaks, which are your clusters.
