Microsoft Technology Licensing, LLC

Intel Corporation

Telex Communications, Inc.

Hannspree, Inc.

Paul S Bradley

Usama M Fayyad

Markus Peter Diebel

Isaac A Simpson

Charlie Garthwaite

Bridget Cameron

Stuart Ashmun

Elinor J Fulton

Michael Nelson

James Rolfe Yurchenco

Mike M Paull

Allan H Stephan

Cory Alan Reina

Antonio T Latto

David Earl Heckerman

Christopher A Meek

David Maxwell Chickering

Cheng Yong Yang

Matthew D Hern

Tom Lake

Mike Carpenter

Christos Faloutsos

Demetrios Achlioptas

Bassel Ojjeh

James R Yurchenco

Markus Wawryniuk

A method and apparatus is disclosed for the customization of electronic equipment of which portable audio players, portable disc players, portable digital games and laptop computers are representative. The electronic equipment may be fitted with a substantially transparent faceplate that may cover removable inserts. These inserts may be customizable to provide unique decorative appearances to the electronic equipment upon which they may be fitted. Decorative designs may either be preprinted on the inserts or a user may produce a customized design.

User interchangeable ornamental portions of electronic devices

A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.

Apparatus and accompanying methods for visualizing clusters of data and hierarchical cluster classifications

A computer data processing system. A method for clustering data in a database comprising providing a database having a number of data records having both discrete and continuous attributes. Grouping together data records from the database which have specified discrete attribute configurations. Clustering data records having the same or similar specified discrete attribute configuration based on the continuous attributes to produce an intermediate set of data clusters. And, merging together clusters from the intermediate set of data clusters to produce a clustering model.

Clustering of databases having mixed data attributes

Removable customizable inserts and faceplate for electronic devices

Television set

Method of reducing dimensionality of a set of attributes used to characterize a sparse data set

Retrofitting recommender systems, so that they can scale to large data, is disclosed. The principal notion is to reduce data requirements of existing recommender engines by performing a type of data reduction that minimizes the loss of information given the engine. The reductions covered in this invention are designed to be easily implemented on a database system, and are intended to have minimal impact on an existing implementation of a recommender system. In one embodiment, a method repeats reducing the data by a number of records, until an accuracy threshold or a performance requirement is met. If the accuracy threshold is met first, the method repeats removing a highest-frequency dimension from the data, until the performance requirement is also met. The reduced data is provided to the recommender system, which generates predictions based on the reduced data, and a query. Any dimension previously removed from the data is subsequently added back to the predictions, if the dimension is not already part of the query. In other embodiments, clustering of the data and/or the query is also performed as an alternative mechanism for reduction. An associated method for post-processing recommendations to adjust for clustering is also presented.

Retrofitting recommender system for achieving predetermined performance requirements

One exemplary embodiment of a scalable clustering algorithm accesses a database of records having attributes or data fields of both enumerated discrete and ordered values and brings a portion of the data records into a rapid access memory. A cluster model for the data includes a table of probabilities for the enumerated, discrete data fields of the data records. The cluster model for data fields that are ordered comprises a mean and spread of the cluster. The cluster model is updated from the database records brought into the rapid access memory. At least some of the database records in the rapid access memory are summarized and stored within the rapid access memory. A criteria is then evaluated to determine if further data should be accessed from the database to further cluster data records in the database. Based on the evaluating step, additional database records in the database are accessed and brought into the rapid access memory for further updating of the cluster model.

Scalable system for clustering of large databases having mixed data attributes

A generalization of frequent item sets to error-tolerant frequent item sets (ETF) is disclosed, together with its application in data clustering using error-tolerant frequent item sets to either build clusters or as an initialization technique for standard clustering algorithms. Efficient feasible computational algorithms for computing ETF's from very large databases is presented. In one embodiment, a method determines a plurality of weak ETF's, which are strongly tolerant of errors, and determines a plurality of strong ETF's therefrom, which are less tolerant of errors. The resulting clusters can be used as an initial model for a standard clustering approach, or may themselves be used as the end clusters. In one embodiment, the data covered by the strong clusters is removed from the data, and the process is repeated, until no more weak clusters can be found. Te invention includes methods for constructing ETF's from more general data types: data sets that include categorical discrete, continuous, and binary attributes.

Data clustering using error-tolerant frequent item sets

Iterative validation for efficiently determining error-tolerant frequent itemsets is disclosed. A description of the application of error-tolerant frequent itemsets to efficiently determining clusters as well as initializing clustering algorithms are also given. In one embodiment, a method determines a sample set of error-tolerant frequent itemsets (ETF's) within a uniform random sample of data within a database. This sample set of ETF's is independently validated, so that, for example, spurious ETF's and spurious dimensions within the ETF's can be removed. The validated sample set of ETF's, is added to the set of ETF's for the database. This process is repeated with additional uniform samples that are mutually exclusive from prior uniform samples, to continue building the database's set of ETF's, until no new sample sets can be found. The method is significantly more efficient than disk-based methods in the prior art, and the data clusters found are often not discovered by traditional clustering algorithm in the prior art.

Iterative validation and sampling-based clustering using error-tolerant frequent item sets

MP3 audio player

In one exemplary embodiment the invention provides a data mining system for use in finding cluster of data items in a database or any other data storage medium. A portion of the data in the database is read from a storage medium and brought into a rapid access memory buffer whose size is determined by the user or operating system depending on available memory resources. Data contained in the data buffer is used to update the original model data distributions in each of the K clusters in a clustering model. Some of the data belonging to a cluster is summarized or compressed and stored as a reduced form of the data representing sufficient statistics of the data. More data is accessed from the database and the models are updated. An updated set of parameters for the clusters is determined from the summarized data (sufficient statistics) and the newly acquired data. Stopping criteria are evaluated to determine if further data should be accessed from the database. Each time the data is read from the database, a holdout set of data is used to evaluate the model then current as well as other possible cluster models chosen from a candidate set of cluster models. The evaluation of the holdout data set allows a cluster model with a different cluster number K&prime; to be chosen if that model more accurately models the data based upon the evaluation of the holdout set.

Varying cluster number in a scalable clustering system for use with large databases

Digital audio player charging station

Growing community of inventors

Seattle, WA, United States of America

Paul S Bradley