A hybrid clustering algorithm for data mining
This is an incremental improvement for data mining applications.
The authors tackled the problem of data clustering by proposing a hybrid algorithm combining K-means and K-harmonic mean, which achieved better performance than traditional methods on five datasets.
Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast and accurate clustering. Its performance is compared with the traditional K-means & KHM algorithm. The result obtained from proposed hybrid algorithm is much better than the traditional K-mean & KHM algorithm.