Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data. See-See partridge chick’s optimization (SSPCO) algorithm is a new optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. We propose chaotic map SSPCO optimization method for clustering, which uses a chaotic map to adopt a random sequence with a random starting point as a parameter, the method relies on this parameter to update the positions and velocities of the chicks. In the study, twelve different clustering algorithms were extensively compared on thirteen test data sets. The results indicate that the performance of the Chaotic SSPCO method is significantly better than the performance of other algorithms for data clustering problems.

SSPCO Algorithm, en, Chaotic, Clustering, Clustering Error, Dataset