Abstract
Data mining means extracting knowledge from big amount of data. Data mining techniques include clustering, Association, classification, Prediction, etc. Clustering is an unsupervised technique that is useful for finding groups in data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, datasets with mixed numeric and categorical types of attributes are common in real life data mining applications. The K-Prototype clustering algorithm is one of the most important algorithms for clustering this type of data. This algorithm produces the locally optimal solution that is dependent on the initial prototype selection. This paper presents a new algorithm for data clustering based on K-Prototype and cuckoo search optimization to attain the global optimization.
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