Abstract: One or more computing devices, systems, and/or methods for message classification are provided. For example, a set of messages is clustered into a set of clusters. A cluster comprises messages with similar features (e.g., similar subject lines, message body content, sender information, recipient information, structure, user action such as reading or deleting, spam vote information, etc.). Cluster features are computed for the clusters based upon features of messages within such clusters. A first table, comprising cluster entries corresponding cluster features of clusters, and a second table, comprising message entries corresponding to clusters to which messages are assigned, are created. Message features of a message are created, using the first table and second table, based upon features of the message and cluster features of clusters to which the message is assigned. A message classifier is used to classify the message (e.g., spam, safe, a threat, etc.) based upon the message features.

Abstract: One or more computing devices, systems, and/or methods for message classification are provided. For example, a set of messages is clustered into a set of clusters. A cluster comprises messages with similar features (e.g., similar subject lines, message body content, sender information, recipient information, structure, user action such as reading or deleting, spam vote information, etc.). Cluster features are computed for the clusters based upon features of messages within such clusters. A first table, comprising cluster entries corresponding cluster features of clusters, and a second table, comprising message entries corresponding to clusters to which messages are assigned, are created. Message features of a message are created, using the first table and second table, based upon features of the message and cluster features of clusters to which the message is assigned. A message classifier is used to classify the message (e.g., spam, safe, a threat, etc.) based upon the message features.

Abstract: In one embodiment, a method includes receiving digital media content files. The digital media content has at least one property associated with it. Topically related segments are determined from received content in accordance with one or more property. Topic clusters are generated based on similarities between segments. Topic clusters are compared and clustered from multiple files of the plurality into cluster groups in accordance with a comparison. Cluster groups are associatively stored in a data storage. A search for topic clusters relevant to a particular need is made, and a series of related segments associated with the search are generated for serial display.

Abstract: In one embodiment, digital content labeling includes receiving digital media content. Content is broken into topically homogenous segments, and these segments are clustered in accordance with segment similarities. A topic label is associated by user assignment or user confirmation with a segment in a cluster, and this topic label is propagated to other segments in the same cluster. A label rank may be associated with a label.

Abstract: In one embodiment, a method includes receiving digital media content files. The digital media content has at least one property associated with it. Topically related segments are determined from received content in accordance with one or more property. Topic clusters are generated based on similarities between segments. Topic clusters are compared and clustered from multiple files of the plurality into cluster groups in accordance with a comparison. Cluster groups are associatively stored in a data storage. A search for topic clusters relevant to a particular need is made, and a series of related segments associated with the search are generated for serial display.