Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.

In this paper, we introduce an incremental nested partition algorithm for finding the inner structuralization of dynamic datasets. Here we use three partition criteria that allow to obtain a hierarchy of clusterings. The algorithm is based on some mathematical properties, which are introduced in the paper. The experimental results over the AFP and TDT2 news collections show the usefulness of our method to reveal different levels of the information hidden in the datasets.

In developed countries, especially over the last decade, there has been an explosive growth in the capability to generate, collect and use very large data sets. The objects of these data sets could be simultaneously described by quantitative and qualitative attributes. At present, algorithms able to process either very large data sets (in metric spaces) or mixed (qualitative and quantitative) incomplete data (missing value) sets have been developed, but not for very large mixed incomplete data sets. In this paper we introduce a new clustering method named GLC+ to process very large mixed incomplete data sets in order to obtain a partition in connected sets.