Abstract: Object clustering methods, ensemble clustering methods, data processing apparatuses, and articles of manufacture are described according to some aspects.

Abstract: A method of labeling in steered visual analysis of a collection of documents, the method comprising receiving a query against a database including a collection of documents; representing contents of the query as a matrix; rotating document vectors associated with respective documents to match the matrix to produce a matrix of rotated document vectors; grouping the rotated document vectors into clusters; and displaying a graphic around an area corresponding to a query term.

Abstract: A method for steering the analysis of a collection of documents includes receiving query terms for use in querying a database including a collection of documents; representing at least some of the query terms in a matrix; rotating document vectors associated with the documents to match the matrix to produce a matrix of rotated document vectors, each document vector representing a numeric vector created in association with individual documents; grouping the rotated document vectors into clusters, each cluster having one or more documents; and projecting the clusters to display visual information of the documents, the visual information including a summary view of the collection of documents. Program code and a system are also provided.

Abstract: A system or method consistent with an embodiment of the present invention is useful in analyzing large volumes of different types of data, such as textual data, numeric data, categorical data, or sequential string data, for use in identifying relationships among the data types or different operations that have been performed on the data. A system or method consistent with the present invention determines and displays the relative content and context of related information and is operative to aid in identifying relationships among disparate data types. Various data types, such as numerical data, protein and DNA sequence data, categorical information, and textual information, such as annotations associated with the numerical data or research papers may be correlated for visual analysis. A variety of user-selectable views may be correlated for user interaction to identify relationships that exist among the different types of data or various operations performed on the data.

Abstract: A system or method consistent with an embodiment of the present invention is useful in analyzing large volumes of different types of data, such as textual data, numeric data, categorical data, or sequential string data, for use in identifying relationships among the data types or different operations that have been performed on the data. A system or method consistent with the present invention determines and displays the relative content and context of related information and is operative to aid in identifying relationships among disparate data types. Various data types, such as numerical data, protein and DNA sequence data, categorical information, and textual information, such as annotations associated with the numerical data or research papers may be correlated for visual analysis. A variety of user-selectable views may be correlated for user interaction to identify relationships that exist among the different types of data or various operations performed on the data.