Incremental learning based multiobjective fuzzy clustering for categorical data

• Categorical data clustering.
• Development of new multiobjective algorithm for clustering categorical data.
• Use of random forest in incremental learning based multiobjective fuzzy clustering.
• New technique for clustering to evolve the best solution from the set of Pareto-optimum solutions.

The problem of clustering categorical data, where attribute values cannot be naturally ordered as numerical values, has gained more importance in recent time. Due to the special properties of categorical attributes, the clustering of categorical data seems to be more complicated than that of numerical data. Although, a few clustering algorithms that optimize single clustering objective have been proposed. It has found that such single measure may not be appropriate for all kind of datasets. Hence, in this article, an Incremental Learning based Multiobjective Fuzzy Clustering for Categorical Data is proposed. For this purpose, a multiobjective modified differential evolution based fuzzy clustering algorithm is developed. Thereafter, it integrates with the well-known supervised classifier, called random forest, using incremental learning to propose the aforementioned technique. Here, the multiobjective algorithm produces a set of optimal clustering solutions, known as Pareto-optimal solutions, by optimizing two conflicting objectives simultaneously. Subsequently, through incremental learning using random forest classifier final solution is evolved from the ensemble Pareto-optimal solutions. The results of the proposed method are demonstrated quantitatively and visually in comparison with widely used state-of-the-art methods for six synthetic and four real life datasets. Finally, statistical test is conducted to show the superiority of the results produced by the proposed method.