Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm

• This paper presents a new optimization approach for data clustering with COA.
• High quality results obtained for dataset.
• This paper presents a new optimization approach for data clustering with COA and fuzzy system for clustering data.
• An efficient proposal for data clustering by Cuckoo Optimization Algorithm.
• In this proposal at each iteration, firstly generates r cuckoo's agents. Each cuckoo generates a random solution string and tries to calculate a fitness value for its solution.

Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.

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