Abstract: Advance Machine Learning or Unsupervised Machine Learning Techniques are provided that relate to Self-learning processes by which a machine generates a sensible automated summary, extracts knowledge, and extracts contextually related Topics along with the justification that explains “why they are related” automatically without any human intervention or guidance (backed ontology's) during the process. Such processes also relate to generating a 360-Degree Contextual Result (360-DCR) using Auto-summary, Knowledge Extraction and Contextual Mapping.

Abstract: Advance Machine Learning or Unsupervized Machine Learning Techniques are provided that relate to Self-learning processes by which a machine generates a sensible automated summary, extracts knowledge, and extracts contextually related Topics along with the justification that explains “why they are related” automatically without any human intervention or guidance (backed ontology's) during the process. Such processes also relate to generating a 360-Degree Contextual Result (360-DCR) using Auto-summary, Knowledge Extraction and Contextual Mapping.

Abstract: The present invention relates to Latent Metonymical analysis and Indexing (LMai) is a novel concept for Advance Machine Learning or Unsupervised Machine Learning Techniques, which uses a statistical approach to identify the relationship between the words in a set of given documents (Unstructured Data). This approach does not necessarily need training data to make decisions on matching the related words together but actually has the ability to do the classification by itself. All that is needed is to give the algorithm a set of natural documents. The method is elegant enough to classify the relationships automatically without any human guidance during the process as shown in FIGS. 6 and 7.

Abstract: Advance Machine Learning or Unsupervised Machine Learning Techniques are provided that relate to Self-learning processes by which a machine generates a sensible automated summary, extracts knowledge, and extracts contextually related Topics along with the justification that explains “why they are related” automatically without any human intervention or guidance (backed ontology's) during the process. Such processes also relate to generating a 360-Degree Contextual Result (360-DCR) using Auto-summary, Knowledge Extraction and Contextual Mapping.

Abstract: The present invention relates to Latent Metonymical analysis and Indexing (LMai) is a novel concept for Advance Machine Learning or Unsupervised Machine Learning Techniques, which uses a statistical approach to identify the relationship between the words in a set of given documents (Unstructured Data). This approach does not necessarily need training data to make decisions on matching the related words together but actually has the ability to do the classification by itself. All that is needed is to give the algorithm a set of natural documents. The method is elegant enough to classify the relationships automatically without any human guidance during the process as shown in FIGS. 6 and 7.