An inheritable clustering algorithm suited for parameter changing

DBSCAN is a classic density based algorithm and it clusters the data set according to the user input parameters. This work investigates how to inherit the mining results of last time when parameters change. A new incremental clustering algorithm IPC-DBSCAN is proposed, which gets the same result as that of rerunning DBSCAN yet high efficiency is achieved. Theoretical analysis and experimental results show that the proposed method reduces search space greatly and has novel efficiency. By interaction, IPC-DBSCAN gets the most satisfying result quickly and especially suits large volume data set.