Oracle International Corporation

Gary L Drescher

Pavani Kuntala

Mark F Hornick

Marcos M Campos

Pablo Tamayo

Chitra Balwant Bhagwat

Joe Yarmus

A system, method, and computer program product provides a useful measure of the accuracy of a Na&#xef;ve Bayes predictive model and reduced computational expense relative to conventional techniques. A method for measuring accuracy of a Na&#xef;ve Bayes predictive model comprises the steps of receiving a training dataset comprising a plurality of rows of data, building a Na&#xef;ve Bayes predictive model using the training dataset, for each of at least a portion of the plurality of rows of data in the training dataset incrementally untraining the Na&#xef;ve Bayes predictive model using the row of data and determining an accuracy of the incrementally untrained Na&#xef;ve Bayes predictive model, and determining an aggregate accuracy of the Na&#xef;ve Bayes predictive model.

Cross-validation for naive bayes data mining model

A system, method, and computer program product that uses attribute importance (AI) to reduce the time and computation resources required to build data mining models, and which provides a corresponding reduction in the cost of data mining. Attribute importance (AI) involves a process of choosing a subset of the original predictive attributes by eliminating redundant, irrelevant or uninformative ones and identifying those predictor attributes that may be most helpful in making predictions. A new algorithm Predictor Variance is proposed and a method of selecting predictive attributes for a data mining model comprises the steps of receiving a dataset having a plurality of predictor attributes, for each predictor attribute, determining a predictive quality of the predictor attribute, selecting at least one predictor attribute based on the determined predictive quality of the predictor attribute, and building a data mining model including only the selected at least one predictor attribute.

Data mining model building using attribute importance

The present invention provides an efficient method and system for performing data mining on large amounts of data using a multi-threaded Bayesian algorithm. The method for data mining includes receiving data from a database, performing multi-threaded classification on the data using a Na&#xef;ve Bayes model and performing multi-threaded regression on the data based on a Na&#xef;ve Bayes model. One of the multi threaded classification and regression steps may include processing the data according to a batch learning system, processing the data according to an incremental learning system, processing the data according to an online learning system, and processing the data according to a distributed learning system. One of the the multi-threaded classification and regression steps may include developing a set of rules from a set of training data. One of the multi-threaded classification and regression steps may comprise arranging the data into a plurality of groups.

Multi-threaded Na&#xef;ve Bayes algorithm for enterprise-server data mining

The notion of checkpointing model building is introduced to safeguard against the loss of computational results due to abnormal termination of long running computation algorithms. A checkpoint manager initiates a checkpoint, wherein initiation occurs by various scenarios, including dynamically, automatically and manually. A computation module executes the checkpoint on an in progress data computation. The checkpoint manager also monitors the execution of the checkpoint for abnormal termination. Upon a determination of abnormal checkpoint termination, the inability to resume model building from a checkpoint, the checkpoint manger either resumes model build from the checkpoint or aborts the model build.

Checkpoint model building for data mining

The present invention relates to progress notification systems, computer program products and methods of operation thereof, that reports processing progress of data mining operations at regular periodic intervals. The system comprises: an input/output interface for exchanging information with a network; a memory for storing updated progress objects associated with the data mining operation as a set of data mining algorithms progress in processing; and a processor coupled to the input/output interface and the memory, the processor for performing the data mining operation, the data mining operation implementing the set of data mining algorithms; and generating a notification object for the data mining operation at a pre-determined interval, the notification object based on the progress objects at each of the pre-determined intervals.

Progress notification supporting data mining

A method, system, and computer program product for generating a representation of a data mining model that improves the transparency of data mining models so as to be more easily interpretable by human users. The method comprises the steps of: receiving a dataset, generating a callable version of the data mining model, and generating a tree representing decisional logic of the data mining model using the dataset.

Growing community of inventors

Cambridge, MA, United States of America

Gary L Drescher