Data Mining

Unsupervised Learning Methods

Unsupervised learning methods attempt to extract important patterns from a data set without using any information from the output variable. Clustering analysis, which is one of the unsupervised learning methods, systematically partitions the data set by minimizing within-group variation and maximizing between-group variation. These variations can be measured on the basis of a variety of distance metrics between observations in the data set. Clustering analysis includes hierarchical and nonhierarchical methods.

Hierarchical clustering algorithms provide a dendrogram that represents the hierarchical structure of clusters. At the highest level of this hierarchy is a single cluster that contains all the observations, while at the lowest level are clusters containing a single observation. Examples of hierarchical clustering algorithms are single linkage, complete linkage, average linkage, and Ward’s method.

Nonhierarchical clustering algorithms achieve the purpose of clustering analysis without building a hierarchical structure. The k-means clustering algorithm is one of the most popular nonhierarchical clustering methods. A brief summary of the k-means clustering algorithm is as follows: Given k seed (or starting) points, each observation is assigned to one of the k seed points close to the observation, which creates k clusters. Then, seed points are replaced with the mean of the currently assigned clusters. This procedure is repeated with updated seed points until the assignments do not change. The results of the k-means clustering algorithm depend on the distance metrics, the number of clusters (k), and the location of seed points. Other nonhierarchical clustering algorithms include k-medoids and self-organizing maps.

Principal components analysis (PCA) is another unsupervised technique and is widely used, primarily for dimensional reduction and visualization. PCA is concerned with the covariance matrix of original variables, and the eigenvalues and eigenvectors are obtained from the covariance matrix. The product of the eigenvector corresponding to the largest eigenvalue and the original data matrix leads to the first principal component (PC), which expresses the maximum variance of the data set. The second PC is then obtained via the eigenvector corresponding to the second largest eigenvalue, and this process is repeated N times to obtain N PCs, where N is the number of variables in the data set. The PCs are uncorrelated to each other, and generally, the first few PCs are sufficient to account for most of the variations. Thus, the PCA plot of observations using these first few PC axes facilitates visualization of high-dimensional data sets.

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