A New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

Impact of Rapid Transit on the Residential Market in Hudson County, New Jersey

The New York Metropolitan region is one of the most populous urban agglomerations in the world, and the single largest in North America.[1] It is also one of the most prominent economic centers, with New York City at the epicenter of its growth. With the entire region growing rapidly over the last decade, it is essential to analyze the socio-economic changes in order to understand the impact it has on commercial real estate. With its focus on housing rentals, this study aims to highlight housing costs as a function of rapid transit over time.