Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together to define a set. Overlapping members in the groups may be further differentiated from one another by partitioning. An optimized partition of set S of N overlapping groups yields a maximum strength for groups and members in that partition. Among other things, the optimized partition includes relative strengths of every individual member in every possible partition and weighting functions applied to the relative strengths and to subgroups of files within the partitions.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon.

Abstract: A plurality of original files represent underlying original bits of data from which a key is created in a multi-dimensional mapping space for a relevancy topic. If new data is sufficiently close to this relevancy topic key, it is related to the topic and presented to users. Various closeness measures are defined as are methods for key creation. Still other features contemplate computing arrangements and program products.

Abstract: Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are arranged, first “patterns” of data are created that further define digital spectrums of new files. It is within this sorted new data that emergent relationships or second “patterns” are examined, according to the techniques for its underlying files, or “patterns of patterns.” Representatively, original files are stored on computing devices. If encoded, they have pluralities of symbols representing an underlying data stream of original bits of data. The original files are examined for relationships between each of the files. The original relationships are converted to new files. The new files are representatively encoded and examined for other relationships.

Abstract: Methods and apparatus teach a digital spectrum of a file representing underlying original data. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are grouped together they can be used to indicate relevance in current data under consideration on a same or different computing device. Also, relevance can be found without traditional notions of needing structured data or users initiating searching for relevance or by examining metadata/administrative information associated with the files. A plurality of original files represent underlying original bits of data from which a key is created in a mapping space for a relevancy topic. If new data is sufficiently close to this, it is related to the topic and presented to users. Various closeness measures are defined as are methods for key creation. Still other features contemplate computing arrangements and program products.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together, while others are differentiated. Grouping ceases upon application of a stopping function so that rightly sized, optimum numbers of file groups are obtained. Embodiments of stopping functions relate to curve types in a mapping of numbers of groups per sequential rounds of grouping, recognizing whether groups have overlapping file members or not, and/or determining whether groups meet predetermined numbers of members, to name a few. Properly grouped files can then be further acted upon.

Abstract: Methods and apparatus teach a digital spectrum of a data file. The digital spectrum is used to map a file's position in multi-dimensional space. This position relative to another file's position reveals closest neighbors. Certain of the closest neighbors are grouped together to define a set. Overlapping members in the groups may be further differentiated from one another by partitioning. An optimized partition of set S of N overlapping groups yields a maximum strength for groups and members in that partition. Among other things, the optimized partition includes relative strengths of every individual member in every possible partition and weighting functions applied to the relative strengths and to subgroups of files within the partitions.

Abstract: Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position. This position relative to another file's position reveals closest neighbors. When multiple such neighbors are arranged, first “patterns” of data are created that further define digital spectrums of new files. It is within this sorted new data that emergent relationships or second “patterns” are examined, according to the techniques for its underlying files, or “patterns of patterns.” Representatively, original files are stored on computing devices. If encoded, they have pluralities of symbols representing an underlying data stream of original bits of data. The original files are examined for relationships between each of the files. The original relationships are converted to new files. The new files are representatively encoded and examined for other relationships.