Knowledge-based data mining

Abstract

We describe techniques for combining two types of knowledge systems: expert and machine learning. Both the expert system and the learning system represent information by logical decision rules or trees. Unlike the classical views of knowledge-base evaluation or refinement, our view accepts the contents of the knowledge base as completely correct. The knowledge base and the results of its stored cases will provide direction for the discovery of new relationships in the form of newly induced decision rules. An expert system called SEAS was built to discover sales leads for computer products and solutions. The system interviews executives by asking questions, and based on the responses, recommends products that may improve a business' operations. Leveraging this expert system, we record the results of the interviews and the program's recommendations. The very same data stored by the expert system is used to find new predictive rules. Among the potential advantages of this approach are (a) the capability to spot new sales trends and (b) the substitution of less expensive probabilistic rules that use database data instead of interviews.

A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection

“…This finding corroborates with conclusions drawn by [30,33,9]. However, the methodologies put forward by these authors typically require very timeconsuming and demanding information-extraction tasks from the experts.…”

“…Weiss et al [30] build a DMS using only expert knowledge as input data to predict promising sales leads. Sinha and Zhao [9] combine expert knowledge with a DMS in the context of credit ratings.…”

“…First, our method does not only rely on expert opinions in the DSS (e.g., [30]). Moreover it overcomes the drawbacks of Sinha and Zhao [9]'s approach in which domain experts have to evaluate every unit of analysis.…”

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

“…The key point is that these two approaches, knowledge elicitation from experts and knowledge discovery from data, complement each other (da Silva, Amorim, Campos, & Brasil, 2002;Daniels & van Dissel, 2002;de la Vega et al, 2010;Weiss, Buckley, Kapoor, & Damgaard, 2003). Applied together, they can be used to build better systems: data mining techniques can be used to support the different tasks involved in expert system (ES) or knowledge-based system (KBS) development (Flior et al, 2010;Mejia-Lavalle & Rodriguez-Ortiz, 1998;Phuong, Phong, Santiprabhob, & Baets, 2001;Wang, Liu, & Cheng, 2004), and expert knowledge can be used to facilitate and improve the results of the different stages of the KDD process (Kusiak & Shah, 2006;Zhang & Figueiredo, 2006).…”

Towards a Better Integration of Data Mining and Decision Support via Computational Intelligence

“…Work in meta-learning is also quite relevant as it attempts to support DM: see METAL (www.metal-kdd.org), [6], and [24]. Other recent work in the field of KDD (Knowledge Discovery in Databases) is that of [13] in which an ontology is used to model domain knowledge to support the KDD process, that of [20] where an environment for the rapid development of pre-DM processing chains is introduced, and that of [25] in which expert system and machine learning technologies are combined to support DM. Work dealing with conceptual queries and online/Web (interactive) DM is also of interest since it must take into account some elements of the DS dimension: see [9], for instance.…”