Knowledge discovery and data mining deal with the task of finding useful information and especially rules in unstructured data. Most knowledge discovery approaches associate conditional probabilities to discovered rules in order to specify their strength. In this paper, we propose a qualitative approach to knowledge discovery. We do so by abstracting from actual probabilities to qualitative inf...

Preservation of needed privacy from mining algorithms (data mining methods which extract information from the privacy diffusion of people and organizations) is an emerging research area. Researchers are creating procedures to maintain a proper balance between maintaining information privacy and knowledge discovery by using data mining. In this paper, we initially use the prefixspan algorithm to...

Exploratory rule discovery, as exemplified by association rule discovery, is has proven very popular. In this paper I investigate issues surrounding the statistical validity of rules found using this approach and methods that might be employed to deliver statistically sound exploratory rule discovery.

The actual trends concerning enterprise development take heavily into account the Software as a Service paradigm. Business rules are widely used to define business behavior. Therefore, to be able to access business rules in this context a proper way of discovery and invocation is required. This paper describes the architecture, and business processes of a Semantic Web Service based registry for...

In this paper, we survey efforts devoted to discovering interesting exceptions from data in data mining. An exception differs from the rest of data and thus is interesting and can be a clue for further discoveries. We classify methods into exception instance discovery, exception rule discovery, and exception structured-rules discovery and give a condensed and comprehensive introduction.