Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

Abstract: A method and apparatus are presented for predicting the behavior or state of a negative occurrence class by scoring histories of members of the negative occurrence class against pasts of members of a positive occurrence class. The method and apparatus predicts the members of the negative occurrence class that are most likely to next transition to members of the positive occurrence class.

Abstract: In a computerized hybrid modeling method and a computer program product for implementing the method, two classification techniques are integrated: expert elicited Bayesian networks and decision trees induced from data. Bayesian networks are a compact representation for probabilistic models and inference. They have been used successfully for many applications involving classification. The tree-based classifiers, on the other hand, have proven their ability to perform well in real world data under uncertainty. For classification purposes, the inference algorithms to compute the exact posterior probability of a target node, given observed evidence in a Bayesian network, are usually computationally intensive or impossible in a mixed model. In those cases, either the approximate results are computed using stochastic simulation methods or the model is approximated using discretization or Gaussian mixture before applying an exact inference algorithm.

Abstract: A method and system has an architecture that employs a unique hybrid approach for data mining that integrates advanced three-dimensional computer visualization and inference-based data generalization techniques. The present method and system is geared towards the interactive acquisition and display of visual knowledge representations. Knowledge representations, called knowledge landscapes, are employed for robust real-time classification of incoming data as well as for forecasting new unexpected trends. Knowledge landscape visualization techniques contribute to better human decision-making insights through facilitation of spatial operations such as navigation and zoom operations. A graphically appealing human computer interface and capability to visualize large and complex knowledge bases through spatial and graphical depictions of knowledge components adds to advantages and widespread applicability.

Abstract: A method for determining supply chain risks is provided. The method including the steps of: providing a plurality of data locations, each data location having an agent and data elements; performing distributed data mining by each of the agents using the data elements at the respective data location to produce a candidate decision for the respective location; determining a global decision from the candidate decisions, the global decision covering the data elements at all of the data locations; and generating predictive risk scores for the data elements from the global decision.

Abstract: A method for distributed data clustering is provided. The method includes the steps of providing data points each having at least one attribute, determining a two class set of data including data to be clustered and non-cluster data, determining an overall best attribute selection from each of a plurality of clustering agents whereby the overall best attribute selection has the highest overall information gain containing data to be clustered, creating a rule based on the overall best attribute, splitting the data points into at least two groups, creating a plurality of subsets wherein each subset contains data from only one class and outputting complete rules whereby the data points are all located in the subsets.

Abstract: A distributed data mining method and system includes a mediator and a plurality of agents, each of said plurality of agents having a local database. The mediator invokes the agents and each agent performs an attribute/value selection process. The agents pass their respective best attribute/value pair to the mediator and the mediator determines a winning agent from the submissions. The agents are notified of the winning selection and the winner then begins data splitting based on the willing attribute/value pair. The winning agent forwards a split information index to the mediator. The mediator provides the split information index to other, non-winning agents and the agents generate rules for the data mining.

Abstract: In a computerized hybrid modeling method and a computer program product for implementing the method, two classification techniques are integrated: expert elicited Bayesian networks and decision trees induced from data. Bayesian networks are a compact representation for probabilistic models and inference. They have been used successfully for many applications involving classification. The tree-based classifiers, on the other hand, have proven their ability to perform well in real world data under uncertainty. For classification purposes, the inference algorithms to compute the exact posterior probability of a target node, given observed evidence in a Bayesian network, are usually computationally intensive or impossible in a mixed model. In those cases, either the approximate results are computed using stochastic simulation methods or the model is approximated using discretization or Gaussian mixture before applying an exact inference algorithm.

Abstract: A distributed data mining method and system includes a mediator and a plurality of agents, each of said plurality of agents having a local database. The mediator invokes the agents and each agent performs an attribute/value selection process. The agents pass their respective best attribute/value pair to the mediator and the mediator determines a winning agent from the submissions. The agents are notified of the winning selection and the winner then begins data splitting based on the willing attribute/value pair. The winning agent forwards a split information index to the mediator. The mediator provides the split information index to other, non-winning agents and the agents generate rules for the data mining.

Abstract: A method and system has an architecture that employs a unique hybrid approach for data mining that integrates advanced three-dimensional computer visualization and inference-based data generalization techniques. The present method and system is geared towards the interactive acquisition and display of visual knowledge representations. Knowledge representations, called knowledge landscapes, are employed for robust real-time classification of incoming data as well as for forecasting new unexpected trends. Knowledge landscape visualization techniques contribute to better human decision-making insights through facilitation of spatial operations such as navigation and zoom operations. A graphically appealing human computer interface and capability to visualize large and complex knowledge bases through spatial and graphical depictions of knowledge components adds to advantages and widespread applicability.