| dc.description.abstract | Many approaches have been proposed to discover useful information patterns from databases, such as concept description, associations, sequential patterns, classification, clustering, and deviation detection. This research proposes a new type of information pattern, called typical patterns, which can provide decision makers with a better understanding of a given dataset. Suppose we are given a dataset containing n objects, each of which is described by a set of attribute values. Mining typical patterns is to select a small subset of objects, say k objects, from these n objects so that these k chosen objects are a compact and suitable representation of the original dataset. Accordingly, the Typical Patterns Mining (TPM) algorithms have been developed to mine typical patterns from databases. Also, extensive experiments have been carried out using real datasets to demonstrate the usefulness of typical patterns in practical situations. Then, although TPM is a good method to automatically determine typical patterns, it lacks ability to accommodate user’s experience and domain knowledge, which are very crucial for making decision in a dynamic business environment. Therefore, this research also develops a dynamic and interactive approach for typical pattern mining, called interactive Typical Pattern Mining (iTPM). In this approach, we accommodate users’ experiences and knowledge by allowing users to iteratively adjust the parameters during the interactive process. Then, an iTPM system is developed to mine typical journals of IS field. The results of experiments indicate that iTPM is more effective than the previous static approach. | en_US |