Abstract: A method for generating a trip prediction specific to a given user includes acquiring a first dataset of trip histories taken in a given transportation network; dividing a trip history of a given user at a specific time point into user training and validation datasets; acquiring training datasets each associated with candidate neighboring users; identifying useful neighbors from the training and validation datasets; combining the user trip history and the trip history of each useful neighbor; applying a similarity function to the combined dataset, wherein a sum of similarities between a given trip and all other trips in the combined dataset is computed; associating a trip having the highest weighted similarity (weighted by frequency) with a prediction for a future trip; and outputting the prediction to an associated user device.

Abstract: A method is disclosed for computing a path-based distance between a pair of nodes in an undirected graph comprising a set of nodes and a set of edges connecting pairs of nodes in the undirected graph with each edge having an edge weight. The method includes: computing a minimum spanning tree (MST) over the undirected graph; computing a path-based distance between the pair of nodes in the MST; and setting the path-based distance between the pair of nodes in the undirected graph equal to the path-based distance between the pair of nodes in the MST. The path-based distance between the pair of nodes in the MST is computed as the largest edge weight along the only path between the pair of nodes in the minimum spanning tree. A similarity matrix may be constructed from the path-based distances, and used for spectral clustering.

Abstract: A method is disclosed for computing a path-based distance between a pair of nodes in an undirected graph comprising a set of nodes and a set of edges connecting pairs of nodes in the undirected graph with each edge having an edge weight. The method includes: computing a minimum spanning tree (MST) over the undirected graph; computing a path-based distance between the pair of nodes in the MST; and setting the path-based distance between the pair of nodes in the undirected graph equal to the path-based distance between the pair of nodes in the MST. The path-based distance between the pair of nodes in the MST is computed as the largest edge weight along the only path between the pair of nodes in the minimum spanning tree. A similarity matrix may be constructed from the path-based distances, and used for spectral clustering.

Abstract: A computer-implemented method for data clustering iteratively partitions a dataset into a predetermined number of clusters. At each of a plurality of iterations, replicator dynamics is performed on the objects of newly-created clusters for a predetermined number of iterations to solve an objective function. For each of these clusters, a cut-off is computed, based on a characteristic vector of the solved objective function. One of the clusters in the current set of clusters which provides most gain to the objective function when that cluster is split into two new clusters, based on the respective cut-off, is selected. The selected one of the two clusters is split into two new clusters based on the respective cut-off and the two new clusters are added to the current set of clusters. The method thus provides for different cut-offs to be used, depending on the cluster being split.