FCM-FCS: Hybridization of Fractional Cuckoo Search with FCM for High Dimensional Data Clustering Process

Abstract

Dimensionality reduction is essential in multidimensional data mining since the dimensionality of real time data could easily reach higher dimensions. Most recent efforts on dimensionality reduction, however, are not adequate to multidimensional data due to lack of scalability. In this paper, we improve the performance of the fractional cuckoo search algorithm by utilizing the FCM function as objective function and we utilize the FCM operator which helps to improve the fitness value of worse solution. The fractional cuckoo search (FCS) algorithm is modified to reduce the dimension and select the best dimension. Once the high dimensional data is reduced in to low dimensional data, then the data is supplied to the clustering algorithm to make the partition easily. Finally, the experimentation is made with synthetic and real datasets and has been we have proved that efficiency of the FCM-FCS algorithm is 1.21% better than FPSO algorithm on iris dataset. The proposed algorithm is 0.66% better than FPSO 4.91% better than FCS on wine dataset and 3.4% better than FPSO 0.1% better than FCS on synthetic dataset.