A New Supervised Clustering Algorithm for Data Set with Mixed Attributes
Shijin Li, Jing Liu, Yuelong Zhu, Xiaohua Zhang · 2007
Because of the complexity of data set with mixed attributes, the traditional clustering algorithms appropriate for this kind of dataset are few and the effect of clustering is not good. K-prototype clustering is one of the most commonly used methods in data mining for this kind of data. We borrow the ideas from the multiple classifiers combing technology, use k- prototype as the basis clustering algorithm to design a multi-level clustering ensemble algorithm in this paper, which adoptively selects attributes for re-clustering. Comparison experiments on Adult data set from UCI machine learning data repository show very competitive results and the proposed method is suitable for data editing.