Microsoft Technology Licensing, LLC

Scott Conrad Oveson

Zhaohui Tang

David Earl Heckerman

David Maxwell Chickering

Robert L Rounthwaite

Alexei V Bocharov

Rong Jian Guan

Amir Netz

Pyungchul Kim

C James MacLennan

Ioan Bogdan Crivat

Irina Gorbach

Raman S Iyer

Distribution displays for categories are provided which illuminate the distribution of continuous attributes over all cases in a category, and which provide a histogram of the population of the different states of categorical attributes. An array of such displays by attribute (in one dimension) and category (in another dimension) may be provided. Category diagram displays are also provided for visualizing the different categories, and their distributions, populations, and similarities. These are displayed through different shading of nodes and edges representing categories and the relationship between two categories, and through proximity of nodes.

System and method for visualization of categories

Systems and methods are provided for generating prediction queries to help a user build and execute prediction queries. A user interface (UI) is provided that is easy to use and understand in connection with the generation of a prediction query for data mining. The UI can be instantiated from a variety of disparate sources that may request query building services. While prediction queries and relational queries are quite different, the UI enables prediction queries to be built in a manner that is similar to the way relational queries are built. In one embodiment, the main screen of the UI includes four main components: (1) a table column mapping area, (3) a selection grid area, (4) a query text display area and (5) a query result grid area. In one embodiment, the query text display area and the query result grid area are initially not presented to the user.

Systems and methods for generating prediction queries

The subject disclosure pertains to extensible data mining systems, means, and methodologies. For example, a data mining system is disclosed that supports plug-in or integration of non-native mining algorithms, perhaps provided by third parties, such that they function the same as built-in algorithms. Furthermore, non-native data mining viewers may also be seamlessly integrated into the system for displaying the results of one or more algorithms including those provided by third parties as well as those built-in. Still further yet, support is provided for extending data mining languages to include user-defined functions (UDFs).

Extensible data mining framework

Systems and methods are provided for producing displays of the accuracy of data mining or statistical models that produce associative predictions. For all cases in a testing data set, the model makes predictions and provides associated probabilities. The cases are sorted by their probability of making accurate predictions and a graph is made of the accuracy of the model over various subsets containing the highest probability cases as evaluated by the model. Where a number of probabilities are presented for the predictions in a basket of predictions, those probabilities are combined to yield a probability score for the entire basket. Additionally, the accuracy of a model over different basket sizes may be graphed. The accuracy graph may also be produced for any models making a prediction, by graphing the probability of making accurate predictions and a graph made of the accuracy of the model over various subsets of the data containing the highest probability cases.

System and method for mining model accuracy display

System and method for visualization of continuous attribute values

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Sammamish, WA, United States of America

Scott Conrad Oveson