Meta patterns: discovering rough classifiers

Organizational memory in today´s business world forms basis for organizational learning, which is the ability of an organization to gain insight and understanding from experience through experimentation, observation, analysis, and a willingness to examine both successes and failures. This basically requires considering different aspects of knowledge that may reside on top of a conventional information management system. Of them, representational and retrieval issues of meta patterns constitute to the main theme of this article. Particularly we are interested in a formal approach to handle rough concepts. A user can inquire persistent concepts by expressing his/her information needs either by a set of rules in horn form constituting a concept or by search terms and their relationships. In turn, a concept may be defined characteristically or distinctively. The user may focus on either common properties of a concept and thus accordingly provide its definition by rules or its distinctive properties using rough classification methods. In this article, we propose a preliminary framework based on minimal term sets with p-norms to extract meta patterns.