مشخصات پژوهش

Partitional data clustering with K-means algorithm is the dividing of objects into smaller and disjoint groups that has the most similarity with objects in a group and most dissimilarity from the objects of other groups. Several techniques have been proposed to avoid the major limitations of K-Means such as sensitive to initialization and easily converges to local optima. An alternative to solve the drawback of the sensitive to centroids’ initialization in K-Means is the K-Harmonic Means (KHM) clustering algorithm. However, KHM is sensitive to the noise and easily runs into local optima. In this work, an improved Cuckoo Search (ICS) algorithm along with particle swarm optimization (PSO) method is proposed based on the K-Harmonic Means (KHM) clustering algorithms. ICS is intended to global optimum solution using Lévy flight method through changing radius in a dynamic and shrewd manner. ICS is effected with PSO to find global optima with fast convergence. The proposed algorithm, called ICMPKHM, solves the local optima problem of KHM with significant improvement on efficacy. Experiments with benchmark datasets show that the proposed algorithm is quite insensitive to the centroids’ initialization. Comparative studies with other algorithms reveal that the proposed algorithm produce better clustering results.