Generating consensus priority point vectors: a logarithmic goal programming approach
Computers and Operations Research
Predictive modeling in automotive direct marketing: tools, experiences and open issues
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Dealing with the Expert Inconsistency in Probability Elicitation
IEEE Transactions on Knowledge and Data Engineering
Selection of web sites for online advertising using the AHP
Information and Management
A common framework for deriving preference values from pairwise comparison matrices
Computers and Operations Research
Evaluation of decision trees: a multi-criteria approach
Computers and Operations Research
Understanding software project risk: a cluster analysis
Information and Management
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
Post-pruning in decision tree induction using multiple performance measures
Computers and Operations Research
Post-pruning in regression tree induction: An integrated approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
Prioritization of association rules in data mining: Multiple criteria decision approach
Expert Systems with Applications: An International Journal
The TAME project: towards improvement-oriented software environments
IEEE Transactions on Software Engineering
A Methodology for Collecting Valid Software Engineering Data
IEEE Transactions on Software Engineering
| Hi-index | 12.05 |
The knowledge discovery via data mining process (KDDM) is a multiple phase that aims to at a minimum semi-automatically extract new knowledge from existing datasets. For many data mining tasks, the evaluation phase is a challenging one for various reasons. Given this challenge several studies have presented techniques that could be used for the semi-automated evaluation of data mining results. When taken together, these studies suggest the possibility of a common multi-criteria evaluation framework. The use of such a multi-criteria evaluation framework, however, requires that relevant objectives, measures and preference function be identified. This implies that the context of the DM problem is particularly important for the evaluation phase of the KDDM process. Our framework utilizes and integrates a pair of established tightly coupled techniques (i.e. Value Focused Thinking (VFT) and the Goal-Question-Metric (GQM) methods) as well as established techniques from multi-criteria decision analysis in order to explicate and utilize context information in order to facilitate semi-automated evaluation.