Cluster analysis using optimization algorithms with newly designed objective functions

• Perform cluster analysis using three newly designed objective functions.
• Utilize three optimization algorithms like, genetic, cuckoo search and PSO.
• Present 21 different clustering algorithms and the validation with 16 datasets.
• Proved, objective function decides effectiveness & search algorithm decides efficiency.
• Presented a suggestion for a better algorithm based on input data characteristics.

Clustering finds various applications in the field of medical and telecommunication for unsupervised learning which is much required in expert system and its application. Various algorithms have been developed to clustering for the past fifty years after the introduction of k-means clustering. Recently, optimization algorithms are applied for clustering to find optimal clusters with the help of different objective functions. Accordingly, in this research, clustering is performed using three newly designed objective functions along with four existing objective functions with the help of optimization algorithms like, genetic algorithm, cuckoo search and particle swarm optimization algorithm. Here, three different objective functions are designed including the cumulative summation of fuzzy membership and distance value with normal data space, kernel space as well as multiple kernel space. In addition to the existing seven objective functions, totally, 21 different clustering algorithms are discussed and the performance is validated with 16 different datasets which are synthetic, small and large scale real data. The comparison is made with five different evaluation metrics to validate the effectiveness and efficiency. From the research outcome, the suggestion is presented to select a suitable algorithm among 21 algorithms for a particular data and results proved that the effectiveness of cluster analysis is mainly dependent on objective function and the efficiency of cluster analysis is based on search algorithm.