Granular computing, as an emerging research field, provides a conceptual framework for studying many issues in data mining. This paper examines some of those issues, including data and knowledge representation and processing. It is demonstrated that one of the fundamental tasks of data mining is searching for the right level of granularity in data and knowledge representation.
The main stream of research in data mining (or knowledge discovery in databases) focuses on algorithms and automatic or semi-automatic processes for discovering knowledge hidden in data. In this paper, we adopt a more general and goal oriented view of data mining. Data mining is regarded as a field of study covering the theories, methodologies, techniques, and activities with the goal of discovering new and useful knowledge. One of its objectives is to design and implement data mining systems. A miner solves problems of data mining manually, or semi-automatically by using such systems. However, there is a lack of studies on how to assist a miner in solving data mining problems. From the experiences and lessons of decision support systems, we introduce the concept of data mining support systems (DMSS). We draw an analogy between the field of decision-making and the field of data mining, and between the role of a manager and the role of a data miner. A DMSS is an active and highly interactive computer system that assists data mining activities. The needs and the basic features of DMSS are discussed.
Information tables provide a convenient and useful tool for representing a set of objects using a group of attributes. This notion is enriched by introducing neighborhood systems on attribute values. The neighborhood systems represent the semantics relationships between, and knowledge about, attribute values. With added semantics, neighborhood based information tables may provide a more general framework for knowledge discovery, data mining, and information retrieval.