Data clustering is a powerful technique for data analysis that used in many applications. The goal of clustering is to detect groups that objects of each group have the most similarity together. Artificial bee colony (ABC) is a simple algorithm with few control parameters to solve clustering problem. However, traditional ABC algorithm is considered the equal importance for all features, while real world applications carry different importance on features. To overcome this issue, we proposed a feature weighting based artificial bee colony (FWABC) algorithm for data clustering. The proposed algorithm considers a specific importance to each feature. The performance of the proposed method has been tested on various datasets and compared to well-known and state-of-the-art methods, the reported results show that the proposed method outperforms other methods.
