Nomenclature and Contemporary Affirmation of the Unsupervised Learning in Text and Document Mining

Document clustering is primarily a method applied for an uncomplicated, document search, analysis and review of content or is a process of automatic classification of documents of similar type categorized to relevant clusters, in a clustering hierarchy. In this paper a review of the related work in the field of document clustering from the simple techniques of word and phrase to the present complex techniques of statistical analysis, machine learning etc are illustrated with their implications for future research work

From text mining to knowledge mining: An integrated framework of concept extraction and categorization for domain ontology

Organizations are struggling with the challenges coming from the regulatory, social and economic environment which are complex and changing continuously. They cause increase demand for the management of organizational knowledge, like how to provide employees, the necessary job-specific knowledge in right time and in right format. Employees have to update their knowledge, improve their competencies continuously. Knowledge repositories have key roles from knowledge management aspects, because they contain primarily the organizations’ intellectual assets (it is explicit knowledge) while employees have tacit knowledge, which is difficult to extract and codify. Business processes are also important from the management of organizational knowledge aspects, they have explicit and tacit knowledge elements as well. One of the key questions is how to handle this hidden knowledge in order to improve the organizational knowledge especially employees' knowledge by providing the most appropriate learning and/or training materials and how can we ensure that the knowledge in business processes are the same as in knowledge repositories and employees' head. These are the major themes in this thesis