Abstract: Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.

Abstract: A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.

Abstract: A method and system are provided. The method includes converting, by a computer having a processor and a memory, categorical sequence data for a customer journey into a numerical similarity matrix. The method further includes learning, by the computer, features of the customer journey by applying a distance metric learning based matrix factorization approach to the numerical similarity matrix.

Abstract: Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.

Abstract: Methods and a system are provided that is performed by a computer server for inferring location context categories for a set of mobile users having at least two members. A method includes, for each mobile user in the set, obtaining at least one location context category therefor from publically available information responsive to uncertain mobile device location data. The method further includes applying multi-user collaborative machine learning to the at least one location context category for each mobile user in the set to infer a single refined location context category for each mobile user in the set.

Abstract: A system and method for dynamic, semi-supervised clustering comprises receiving data attributes, generating a set of ensemble partitions using the data attributes, forming a convex hull using the set of ensemble partitions, generating a simplex vector by performing ensemble clustering on the convex hull, receiving dynamic links, deriving an optimal simplex vector using the simplex vector and the dynamic links, computing a current optimal clustering result using the optimal simplex vector, and outputting the current optimal clustering result.

Abstract: A system, method and computer program product for generating a classification model using original data that is sensitive or private to a data owner. The method includes: receiving, from one or more entities, a masked data set having masked data corresponding to the original sensitive data, and further including a masked feature label set for use in classifying the masked data contents; forming a shared data collection of the masked data and the masked feature label sets received; and training, by a second entity, a classification model from the shared masked data and feature label sets, wherein the classification model learned from the shared masked data and feature label sets is the same as a classification model learned from the original sensitive data. The sensitive features and labels cannot be reliably recovered even when both the masked data and the learning algorithm are known.

Abstract: A method for generating a classification model using original data that is sensitive or private to a data owner. The method includes: receiving, from one or more entities, a masked data set having masked data corresponding to the original sensitive data, and further including a masked feature label set for use in classifying the masked data contents; forming a shared data collection of the masked data and the masked feature label sets received; and training, by a second entity, a classification model from the shared masked data and feature label sets, wherein the classification model learned from the shared masked data and feature label sets is the same as a classification model learned from the original sensitive data. The sensitive features and labels cannot be reliably recovered even when both the masked data and the learning algorithm are known.

Abstract: Systems and methods are disclosed for performing duplicate document analyses to identify texturally identical or similar documents, which may be electronic documents stored within an electronic discovery platform. A process is described which includes representing each of the documents, including a target document, as a relatively large n-tuple vector and also as a relatively small m-tuple vector, performing a series of calculations on the set of m-tuple vectors to identify a set of documents which are candidate near-duplicates to the target document, and then filtering the candidate set of near-duplicate documents based upon the distance of their n-tuple vectors from the n-tuple vector of the target document.

Abstract: A system and method for dynamic, semi-supervised clustering comprises receiving data attributes, generating a set of ensemble partitions using the data attributes, forming a convex hull using the set of ensemble partitions, generating a simplex vector by performing ensemble clustering on the convex hull, receiving dynamic links, deriving an optimal simplex vector using the simplex vector and the dynamic links, computing a current optimal clustering result using the optimal simplex vector, and outputting the current optimal clustering result.

Abstract: Systems and methods are disclosed for performing duplicate document analyses to identify texturally identical or similar documents, which may be electronic documents stored within an electronic discovery platform. A process is described which includes representing each of the documents, including a target document, as a relatively large n-tuple vector and also as a relatively small m-tuple vector, performing a series of calculations on the set of m-tuple vectors to identify a set of documents which are candidate near-duplicates to the target document, and then filtering the candidate set of near-duplicate documents based upon the distance of their n-tuple vectors from the n-tuple vector of the target document.

Abstract: Systems and methods are disclosed for performing duplicate document analyses to identify texturally identical or similar documents, which may be electronic documents stored within an electronic discovery platform. A process is described which includes representing each of the documents, including a target document, as a relatively large n-tuple vector and also as a relatively small m-tuple vector, performing a series of one-dimensional searches on the set of m-tuple vectors to identify a set of documents which are near-duplicates to the target document, and then filtering the near set of near duplicate documents based upon the distance of their n-tuple vectors from that of the target document.