Cluster analysis categorizes a collection of "objects," such as survey respondents, into groups or "clusters" - espcially helpful in identifying potential market segments. A problem with cluster analysis is that every analysis always produces clusters regardless of a true existence of patterns in the data - the fact that a solution seems reasonable is no guarantee that the results would be reproducible with a different sample of customers or variables, or at a different time of the day. Our Convergent Cluster & Ensemble Analysis (CCEA) System addresses this problem. It uses a "k means" method of determining clusters (or for developing a consensus solution, based on the notion of "clustering on clusters") that involves iterating from random but strategically chosen starting points.

CCEA Specifications

• Automatically replicates each analysis 30 times where each replication is compared to every other to assess its reproducibility. The most reproducible solution is chosen automatically, and its level of reproducibility is reported.
• Used with data from any source, formatted in.csv (comma-separated values) files which can be saved easily from common programs such as Excel

System Requirements

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Server Requirements

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