Expanding the meaning of and applications for data mining

Summary form only. Data mining in databases aims to make sense of data by revealing meaningful and easily interpretable relationships. In spite of many existing variations, this research goal permeates the entire area. The domain of data mining is highly heterogeneous embracing a number of well-established information technologies including: statistical pattern recognition, neural networks, machine learning, knowledge-based systems, etc. The synergistic character of data mining is one of its dominant features that makes this pursuit an emerging new area of research and application. By its nature, data mining is very much oriented towards the end-user, thus implying that any results need to be easily interpretable. Granulation of information promotes this interpretability and channels pursuits of data mining, which can be computationally intensive and thus highly prohibitive, towards more efficient and feasible processing of information granules. Finally, an interesting emerging area of data mining involves perception. Data mining often retrieves "perceived data" and the problem is to reconcile this perceived data with the real world. Information granulation can help with this problem.