One of the challenging research problems is to develop data mining and decision support integration techniques and to propose new methods for collaborative data mining. Advances in this area were achieved within the European project Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise (SolEuNet, 2000-2003), in which a virtual enterprise model was proposed as a dynamic problem- solving link between a network of experienced data mining and decision support experts on the one hand, and customers in need of specific solutions on the other.
Key Terms in this Chapter
Data Mining and Decision Support Integration: Aimed at enabling the fusion of knowledge extracted from data and the expert knowledge, and consequently enabling the successful solution of new types of problems.
E-Collaboration Space: The framework proposed by McKenzie and Van Winkelen (2001) for studying organizations working in e-collaboration. It identifies different types of e-collaboration and proposes three delineating dimensions in which e-collaboration is studied: relationships, the task, and the outcomes.
Text and Web Mining: Aim at analysing data in textual form. One of the most popular applications of text mining is document categorization into pre-defined categories based on their content. Other important tasks include document search based on the content, automatic document summarization, automatic construction of document hierarchies, document authorship detection, and identification of plagiarism of documents.
Data Mining: Mainly concerned with analyzing existing data, typically stored in a database or a data warehouse. It is the core of a knowledge discovery process, which aims at the extraction of interesting, non-trivial, implicit, previously unknown, and potentially useful information from data. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, and data/model visualization.
Decision Support: Concerned with developing methods and applications supporting different areas of management including data management, model management, and interface management. Decision support is mostly associated with developing decision support systems aimed at helping decision makers solve problems and make decisions. Decision support systems provide a selection of data analysis, simulation, visualization, and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases, and data warehouses.
CRISP Data Mining Methodology (CRISP-DM): The Cross Industry Standard Process for Data Mining that covers the data analysis process from problem definition to presentation and delivery of the resulting patterns. It was developed by Chapman et al. (2000) and is becoming a de facto industrial standard.