University of Minnesota Ph.D. dissertation.June 2016. Major: Computer Science. Advisor: George Karypis. 1 computer file (PDF); xi, 151 pages.Nearest neighbor graphs (NNGs) contain the set of closest neighbors, and their similarities, for each of the objects in a set of objects. They are widely used in many real-world applications, such as clustering, online advertising, recommender systems, data cleaning, and query refinement. A brute-force method for constructing the graph requires O(n^2) similarity comparisons for a set of n objects. One way to reduce the number of comparisons is to ignore object pairs with low similarity, which are unimportant in many domains. Current methods for construction of the graph tackle the problem by either pruning the similarity search space, avoiding comparisons of objects that can be determined to not meet the similarity bounding conditions, or they solve the problem approximately, which can miss some of the neighbors. This thesis addresses the problem of efficiently constructing the exact nearest neighbor graph for a large set of objects, i.e., the graph that would be found by comparing each object against all other objects in the set. In this context, we address two specific problems. The epsilon-nearest neighbor graph (epsilon-NNG) construction problem, also known as all-pairs similarity search (APSS), seeks to find, for each object, all other objects with a similarity of at least some threshold epsilon. On the other hand, the k-nearest neighbor graph (k-NNG) construction problem seeks to find the k closest other objects to each object in the set. For both problems, we propose filtering techniques that are more effective than previous ones, and efficient serial and parallel algorithms to construct the graph. Our methods are ideally suited for sparse high dimensional data
