Generating granular clusters for real-time processing is provided. The systems can identify tokens based on aggregating input from computing devices over a time interval. The systems can identify, based on metrics, a subset of tokens for cluster generation. The systems can generate, via a clustering technique, token clusters from the subset of the tokens, each of the token clusters comprising two or more tokens from the subset of the tokens. The systems can apply a de-duplication technique to each of the token clusters. The systems can apply a filtering technique to the token clusters to remove tokens erroneously grouped in a token cluster. The systems can assign, based on a selection process, a label for each of the token clusters. The systems can activate, based on a number of remaining tokens in each of the token clusters, a subset of the token clusters for real-time content selection.

Generating granular clusters for real-time processing is provided. The systems can identify tokens based on aggregating input from computing devices over a time interval. The systems can identify, based on metrics, a subset of tokens for cluster generation. The systems can generate, via a clustering technique, token clusters from the subset of the tokens, each of the token clusters comprising two or more tokens from the subset of the tokens. The systems can apply a de-duplication technique to each of the token clusters. The systems can apply a filtering technique to the token clusters to remove tokens erroneously grouped in a token cluster. The systems can assign, based on a selection process, a label for each of the token clusters. The systems can activate, based on a number of remaining tokens in each of the token clusters, a subset of the token clusters for real-time content selection.

A classification unit causes each of a plurality of classifiers trained to classify data of a corresponding class into one of two values through qSVM to classify data for prediction. Further, the calculation unit calculates the energy of the classification result of the data for prediction for each of the plurality of classifiers. Further, the determination unit determines a class of the data for prediction based on the classification result of the classification unit and the energy calculated by the calculation unit.

A classification unit causes each of a plurality of classifiers trained to classify data of a corresponding class into one of two values through qSVM to classify data for prediction. Further, the calculation unit calculates the energy of the classification result of the data for prediction for each of the plurality of classifiers. Further, the determination unit determines a class of the data for prediction based on the classification result of the classification unit and the energy calculated by the calculation unit.

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

In some implementations, a device may receive, from a data stream, a set of data points arranged in a dimensional data space. The device may compare the set of data points to identify one or more clusters using values of a distance parameter for data points included in the set of data points, wherein the values of distance parameter includes different values of the distance parameter for different data points. The device may transmit an indication of the one or more clusters to cause a device to display information associated with the one or more clusters. The device may receive, from the device, feedback information associated with at least one data point, wherein the feedback information indicates that at least one data point is associated with an error. The device may modify a value of the distance parameter associated with the at least one data point to a modified value.

A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.

A method is provided of communication between a user and a database of Patents and also of the display and the interactive exploration of data on information of interest relating to Patents/Patent applications. The method comprises: the generation, by means of an access interface, of a request allowing the database to be interrogated based on at least one selection criterion entered into the access interface; the interrogation of the database by means of the request and the loading of bibliographical data for the Patents/Patent applications found, the downloaded bibliographical data comprising data on the technological category; the processing of the bibliographical data, the processing comprising an analysis of co-occurrences comprising the determination of a number of co-occurrences of data on the technological category for all of the Patents/Patent applications found; the displaying, in interactive graphical and/or textual form, of a result and/or of an interpretation of the analysis of co-occurrences.