Data Mining in Large Databases Using Domain Generalization Graphs
Journal of Intelligent Information Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Ranking the Interestingness of Summaries from Data Mining Systems
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Principles for mining summaries using objective measures of interestingness
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Measuring interestingness of discovered skewed patterns in data cubes
Decision Support Systems
Cooperative discovery of interesting action rules
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Data summarization for network traffic monitoring
Journal of Network and Computer Applications
Discovering diverse-frequent patterns in transactional databases
Proceedings of the 17th International Conference on Management of Data
Hi-index | 0.01 |
Knowledge discovery in databases is used to discover useful and understandable knowledge from large databases. A process of knowledge discovery consists of two steps, the data mining step and the evaluation step. In this paper, evaluating and ranking the interestingness of summaries generated from databases, which is a part of the second step, is studied using diversity measures. Sixteen previously analyzed diversity measures of interestingness are used along with three not previously considered ones, brought from different well-known areas. The latter three measures are evaluated theoretically according to five principles that a measure must satisfy to be qualified acceptable for ranking summaries. A theoretical correlation study between the eight measures that satisfy all five principles is presented based on mathematical proofs. An empirical evaluation is conducted using three real databases. Then, a classification of the eight measures is deduced. The resulting classification is used to reduce the number of measures to only two, which are the best over all criteria, and that produce non-similar results. This helps the user interpret the most important discovered knowledge in his decision making process.