A Hybrid Clustering Algorithm Based on Dimensional Reduction and K-Harmonic Means

Clustering analysis is an active and challenge research direction in the field of data mining. In this paper we propose a new clustering algorithm based on dimensional reduction approach and K-harmonic means algorithm. Numerical results illustrate that the new hybrid clustering algorithm has advantages in the computation time, iteration numbers and clustering results in most cases, and it is also an algorithm which is suitable for large scale data sets.