Self-learning analytical attribute and clustering segmentation system

A computer-implemented method performed by a processor configured to execute instructions to associate a log entry with an attribute identifier stored in non-transitory memory, the method comprising:

• identifying, by using a text classifier, a log description of the log entry and a metafield of the log entry;
• determining, by using the text classifier, whether the log description includes a datafield by comparing the log description with a datafield indicator;
• generating, by using a datafield classifier, datafield metrics for the datafield corresponding to a plurality of attribute identifiers, the datafield metrics including an accuracy value indicating an association between the datafield and the plurality of attribute identifiers;
• generating, by using a metadata classifier, metafield metrics for the metafield corresponding to the plurality of attribute identifiers, the metafield metrics including an accuracy value indicating an association between the metafield and the plurality of attribute identifiers;
• applying, by using a combination classifier, weight values to the datafield metrics and the metafield metrics;
• generating, by using the combination classifier, weighted metrics for the log entry, the weighted metrics including an accuracy value indicating an association between the log entry and the plurality of attribute identifiers;
• evaluating, by using the combination classifier, an attribute importance value of each attribute identifier;
• in response to the evaluation of the attribute importance value, selecting, by using the combination classifier, an attribute identifier of the plurality of attribute identifiers having a greatest attribute importance value;
• and
• generating, by using the combination classifier, a log entry association based on the weighted metrics, the log entry association including an association between the log entry and a corresponding attribute associated with the selected attribute identifier.