Analyzing Big Data with Dynamic Quantum Clustering

Understand

How does one search for a needle in a multi-dimensional haystack without knowing what a needle is and without knowing if there is one in the haystack? This kind of problem requires a paradigm shift - away from hypothesis driven searches of the data - towards a methodology that lets the data speak for itself.

• Dynamic Quantum Clustering (DQC) is such a methodology.
• DQC is a powerful visual method that works with big, high-dimensional data.
• It exploits variations of the density of the data (in feature space) and unearths subsets of the data that exhibit correlations among all the measured variables.