Fuzzy Centroid and Genetic Algorithms: Solutions for Numeric and Categorical Mixed Data Clustering - Nooraeni, Rani; Arsa, Muhamad Iqbal; Kusumo Projo, Nucke Widowati (Procedia computer science, 2021)
Statistical data analysis in machine learning and data mining usually uses the clustering technique. However, data with both attributes or mixed data exists universally in real life. K-prototype is a well-known algorithm for clustering mixed data because of its effectiveness in handling large data. However, practically, k-prototype has two main weaknesses, the use of mode as a cluster center for categorical attributes cannot accurately represent the objects, and the algorithm may stop at the local optimum solution because affected by random initial cluster prototypes. To overcome the first weakness, we can use fuzzy centroid, and for second weakness is to implement the genetic algorithm to search the global optimum solution. Our research combines the genetic algorithm and Fuzzy K-Prototype to accommodate these two weaknesses. We set up two multivariate data with high correlation and low correlation to see the robustness of the proposed algorithm. According to four value indexes of clustering result evaluation, Coefficient Varians Index, Partition Coefficient, Partition Entropy, and Purity, show that our proposed algorithm has a better result than K prototype. Based on the evaluation result, we conclude that our proposed algorithm can solve two weaknesses of the k-prototype algorithm.