Genetic Distance Measure for K-modes Algorithm
نویسندگان
چکیده
K-means algorithm has been shown to be an effective and efficient algorithm for clustering. However, the k-means algorithm is developed for numerical data only. It is not suitable for the clustering of non-numerical data. K-modes algorithm has been developed for clustering categorical objects by extending from the k-means algorithm. However, no one applies this technique for classification of categorical data. In this paper, the k-modes algorithm is introduced for the classification of categorical objects based on Soybean and Nursery databases. Especially, a genetic algorithm is proposed for designing the dissimilarity measure termed Genetic Distance Measure (GDM) such that the performance of the K-modes algorithm may be improved by 10% and 76% for Soybean and Nursery databases compared with the conventional k-modes algorithm.
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