Many approaches have been developed in cluster analysis, which in general fall into two categories: hierarchical and partitive. Cluster analysis is usually used in situations where clustering information is not observed on the data points and one wants to get this information from the data to explicitly group them. Its main objective is to divide a collection of data points, often of multivariate nature, into subsets or вЂњclustersвЂќ such that observations within one cluster are more вЂњsimilarвЂќ (homogeneous) to each other than to observations in different clusters. Cluster analysis is an important unsupervised statistical learning and data mining technique for clustering homogeneous observations from data. In this section, we briefly review cluster analysis but not the familiar regression analysis and then introduce the regression clustering problem. It is, thus, the intention of this paper to focus on statistical estimation, selection, and computing of regression clustering.

However, regression clustering, a data mining method integrating the two, has rarely been studied as a single entity despite its great potential for practical use. Regression and clustering are probably two of the most important statistical data mining methods used in practice including artificial intelligence and neuroscience. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. We further develop a computing procedure for regression clustering estimation and selection. We also provide a model selection based technique to determine the number of regression clusters underlying the data. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model.

The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience.

Partition to test final cut 10.4