<ul data-eligibleForWebStory="true"><li>Researchers introduce novel null models for assessing data mining results using statistical hypothesis testing.</li><li>These null models preserve more properties of observed binary transactional and sequence datasets compared to existing models.</li><li>The new models maintain the Bipartite Joint Degree Matrix of the dataset's corresponding bipartite (multi-)graph.</li><li>Preserving properties like the number of caterpillars (paths of length three) is a focus of the new null models.</li><li>The researchers developed a suite named Alice, leveraging Markov chain Monte Carlo algorithms for sampling datasets from the null models.</li><li>Alice is based on a well-defined set of states and efficient operations for transitioning between them.</li><li>Experimental results demonstrate that Alice mixes quickly, scales effectively, and uncovers different significant results compared to existing models.</li></ul>

Alice and the Caterpillar: A more descriptive null model for assessing data mining results

Discover more