Abstract: Data visualization methods, data visualization devices, data visualization apparatuses, and articles of manufacture are described according to some aspects.

Abstract: Systems and computer-implemented processes for identification of features and determination of feature associations in a group of documents can involve providing a plurality of keywords identified among the terms of at least some of the documents. A value measure can be calculated for each keyword. High-value keywords are defined as those keywords having value measures that exceed a threshold. For each high-value keyword, term-document associations (TDA) are accessed. The TDA characterize measures of association between each term and at least some documents in the group. A processor quantifies similarities between unique pairs of high-value keywords based on the TDA for each respective high-value keyword and generates a similarity matrix that indicates one or more sets that each comprise highly associated high-value keywords.

Abstract: Data visualization methods, data visualization devices, data visualization apparatuses, and articles of manufacture are described according to some aspects.

Abstract: Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

Abstract: Methods and systems for rapid automatic keyword extraction for information retrieval and analysis. Embodiments can include parsing words in an individual document by delimiters, stop words, or both in order to identify candidate keywords. Word scores for each word within the candidate keywords are then calculated based on a function of co-occurrence degree, co-occurrence frequency, or both. Based on a function of the word scores for words within the candidate keyword, a keyword score is calculated for each of the candidate keywords. A portion of the candidate keywords are then extracted as keywords based, at least in part, on the candidate keywords having the highest keyword scores.

Abstract: Systems and computer-implemented processes for computation and analysis of significant themes in a corpus of documents. The computation and analysis of significant themes can be executed on a processor and involves generating a lexical unit document association (LUDA) vector for each lexical unit that has been provided and quantifying similarities between each unique pair of lexical units. The LUDA vector characterizes a measure of association between its corresponding lexical unit and documents in the corpus. The lexical units can then be grouped into clusters such that each cluster contains a set of lexical units that are most similar as determined by the LUDA vectors and a predetermined clustering threshold.

Abstract: Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

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: Systems and methods provide several enhancements for the viewing, analysis, and generation of landscape views in a data analysis system, including: allowing a user to select from multiple methods to generate a landscape view, providing labels for peaks of a landscape, enabling the user to replace labels displayed on the landscape view, enabling a landscape view to be recalculated based on the replacement labels, and allowing a user to switch or morph between two landscape views generated by different methods. Such methods or systems generate graphical landscape map visualizations from a set of data records.

Abstract: A data import system enables access to data of multiple types from multiple data sources of different formats and provides an interface for importing data into a data analysis system. The interface enables a user to customize the formatting of the data as the data is being imported into a data analysis system. A user may select first user defined options for operating on a first data set received during a data importation process. An intermediate representation of the data set is generated based on the user first defined options. A user may specify second user defined options based on the intermediate representation during the data importation process. The second user defined options are processed to produce a final data representation of the data set to be used for analysis of the data. The intermediate representation may be a data table. The processing of a data set may include merging a first and second data set to produce the final data representation.

Abstract: Methods and systems are provided that enable text in various sections of data records to be separately catalogued, indexed, or vectorized for analysis in a text visualization and mining system. A text processing system receives a plurality of data records, where each data record has one or a plurality of attribute fields associated with the records. The attributes fields containing textual information are identified. The specific textual content of each attribute field is identified. An index is generated that associates the textual content contained in each attribute field with the attribute field containing the textual content. The index is operable for use in text processing. The plurality of data records may be located in a data table and the textual information may be contained within cells of the data table. In another aspect, a plurality of data records is received, where at least some of the data records contain text terms.