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Predicting Future Employment , Productivity and Income ( Finding Patterns in Economic Data )

We implement and evaluate several methods for making predictions using, or finding patterns in, heterogeneous economic data. Though both supervised and unsupervised algorithms are used, the primary focus is on k-means clustering and logical extensions for use with non-continuous data. By altering the dissimilarity function and centroid update procedure, k-means can be made to perform reasonably well on mixed or categorical data while retaining the other characteristics of k-mean clustering—namely, relatively good speed on larger data sets. Such a dissimilarity function can be defined either using domain-specific knowledge, or in a generalizable manner.