At the same time, there is a growing realization and expectation that data, intelligently analyzed and presented, should be a valuable resource. There are several ways to classify database mining work. One way is by the amount of semantic domain knowledge used. Supervised methods may use semantic knowledge. Semantic knowledge is used to improve efficiency and guide discovery.
Unsupervised methods use non-semantic heuristics.
However, embedding domain knowledge in a context sensitive structure is difficult. Conversely, it is also difficult to non-semantically recognize the interesting. In general, non-semantic definitions of interestingness need more development. Uncertainty Lack of crispness is an issue. Most mining techniques implicitly assume that the data is clean and that the data can eventually be clustered using a precise metric.