Cluster Ensemble Approach for Clustering Mixed Data

The paper presents a clustering ensemble method based on ensemble clustering approach for mixed data. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a decision fusion function to achieve a more accurate and stable final output.Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attribute and these algorithms were not generally scalable for large datasets. However; data sets with mixed types of attributes are common in real life data mining applications. So a novel divide-andconquer technique is designed and implemented to solve this problem.First, the original mixed dataset is divided into two sub-datasets: the pure categorical dataset and the pure numeric dataset. Next, existing well established clustering algorithms designed for different types of datasets are employed to produce corresponding clusters. Last, the clustering results on the categorical and numeric dataset are combined as a categorical dataset, on which the categorical data clustering algorithm is used to get the final clusters.