Cuckoo optimization approach for data clustering

Clustering is a popular data analysis and data mining technique, which collects a set of objects into different groups. This unsupervised technique has been applied for a wide variety ofproblems in different fields, because of the properties of the clustering methods. In this article, a new cuckoo clustering algorithm (CCA) is proposed based on the special lifestyle of a bird family, called Cuckoo. While k-means algorithm is one of the most popular clustering techniques. It suffers from the sensitivity to the initializations, shapes of clusters and easy to be trapped in local easy to be trapped in local optimal solutions. The proposed algorithm finds the global optimal solution with respect to the specified objective function. Furthermore it has been showed that the proposed method is not sensitive to the shape and size of the clusters and suitable to multi-dimensional data sets. The quality of proposed algorithm is evaluated on some UCI data sets. The experimental results show that the proposed outperforms the other algorithms such as kmeansand PSO.

کلیدواژه ها:

Clustering analysis ، Cuckoo optimization algorithm ، data mining ، nature-inspired strategy