Publications by Author: Justin Grimmer

2016
Method and Apparatus for Selecting Clusterings to Classify a Data Set
Gary King and Justin Grimmer. 12/13/2016. “Method and Apparatus for Selecting Clusterings to Classify a Data Set.” United States of America 9,519,705 B2 (Patent and Trademark Office).Abstract

In a computer assisted clustering method, a clustering space is generated from fixed basis partitiions that embed the entire space of all possible clusterings. A lower dimensional clustering space is created from the space of all possible clusterings by isometrically embedding the space of all possible clusterings in a lower dimensional Euclidean space. This lower dimensional space is then sampled based on the number of documents in the corpus. Partitions are then developed based on the samples that tessellate the space. Finally, using clusterings representative of these tessellations, a two-dimensional representation for users to explore is created.

Patent
2014
You Lie! Patterns of Partisan Taunting in the U.S. Senate (Poster)
Justin Grimmer, Gary King, and Chiara Superti. 2014. “You Lie! Patterns of Partisan Taunting in the U.S. Senate (Poster).” In Society for Political Methodology. Athens, GA.Abstract

This is a poster that describes our analysis of "partisan taunting," the explicit, public, and negative attacks on another political party or its members, usually using vitriolic and derogatory language. We first demonstrate that most projects that hand code text in the social sciences optimize with respect to the wrong criterion, resulting in large, unnecessary biases. We show how to fix this problem and then apply it to taunting. We find empirically that, unlike most claims in the press and the literature, taunting is not inexorably increasing; it appears instead to be a rational political strategy, most often used by those least likely to win by traditional means -- ideological extremists, out-party members when the president is unpopular, and minority party members. However, although taunting appears to be individually rational, it is collectively irrational: Constituents may resonate with one cutting taunt by their Senator, but they might not approve if he or she were devoting large amounts of time to this behavior rather than say trying to solve important national problems. We hope to partially rectify this situation by posting public rankings of Senatorial taunting behavior.

Poster
2013
Method and Apparatus for Selecting Clusterings to Classify A Predetermined Data Set
Gary King and Justin Grimmer. 2013. “Method and Apparatus for Selecting Clusterings to Classify A Predetermined Data Set.” United States of America 8,438,162 (May 7).Abstract

A method for selecting clusterings to classify a predetermined data set of numerical data comprises five steps. First, a plurality of known clustering methods are applied, one at a time, to the data set to generate clusterings for each method. Second, a metric space of clusterings is generated using a metric that measures the similarity between two clusterings. Third, the metric space is projected to a lower dimensional representation useful for visualization. Fourth, a “local cluster ensemble” method generates a clustering for each point in the lower dimensional space. Fifth, an animated visualization method uses the output of the local cluster ensemble method to display the lower dimensional space and to allow a user to move around and explore the space of clustering.

Patent
2011
General Purpose Computer-Assisted Clustering and Conceptualization
Justin Grimmer and Gary King. 2011. “General Purpose Computer-Assisted Clustering and Conceptualization.” Proceedings of the National Academy of Sciences. Publisher's VersionAbstract

We develop a computer-assisted method for the discovery of insightful conceptualizations, in the form of clusterings (i.e., partitions) of input objects. Each of the numerous fully automated methods of cluster analysis proposed in statistics, computer science, and biology optimize a different objective function. Almost all are well defined, but how to determine before the fact which one, if any, will partition a given set of objects in an "insightful" or "useful" way for a given user is unknown and difficult, if not logically impossible. We develop a metric space of partitions from all existing cluster analysis methods applied to a given data set (along with millions of other solutions we add based on combinations of existing clusterings), and enable a user to explore and interact with it, and quickly reveal or prompt useful or insightful conceptualizations. In addition, although uncommon in unsupervised learning problems, we offer and implement evaluation designs that make our computer-assisted approach vulnerable to being proven suboptimal in specific data types. We demonstrate that our approach facilitates more efficient and insightful discovery of useful information than either expert human coders or many existing fully automated methods.

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