Strategy Mining

Strategy mining is a new area of research about discovering strategies for decision-making. It is motivated by how similarity is assessed in retrospect in law. In the legal domain, when both case facts and court decisions are present, it is often useful to assess similarity by accounting for both case facts and case outcomes. In this paper, we formulate the strategy mining problem as a clustering problem with the goal of finding clusters that represent disparate conditional dependency of decision labels on other features. Existing clustering algorithms are inappropriate to cluster dependency because they either assume feature independence, such as K-means, or only consider the co-occurrence of features without explicitly modeling the special dependency of the decision label on other features, such as Latent Dirichlet Allocation (LDA). We propose an Expectation Maximization (EM) style unsupervised learning algorithm for dependency clustering. Like EM, our algorithm is grounded in statistical learning theory. It minimizes the empirical risk of decision tree learning. Unlike other clustering algorithms, our algorithm is irrelevant-feature resistant, and its learned clusters modeled by decision trees are strongly interpretable and predictive. We systematically evaluate both the convergence property and solution quality of our algorithm using a common law dataset comprised of actual cases. Experimental results show that our algorithm significantly outperforms K-means and LDA on clustering dependency