Data mining: concepts and techniques
Data mining: concepts and techniques
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Data Mining and Knowledge Discovery
Assessing the quality of use case descriptions
Software Quality Control
Pattern-based design evolution using graph transformation
Journal of Visual Languages and Computing
Sequential pattern mining for structure-based XML document classification
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Applying transformations to model driven data warehouses
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Evaluating maintainability with code metrics for model-to-model transformations
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
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Model Driven Software Development (MDSD) has matured over the last few years and is now becoming an established technology. Models are used in various contexts, where the possibility to perform different kinds of analyses based on the modelled applications is one of these potentials. In different use cases during these analyses it is necessary to detect patterns within large models. A general analysis technique that deals with lots of data is pattern mining. Different algorithms for different purposes have been developed over time. However, current approaches were not designed to operate on models. With employing QVT for matching and transforming patterns we present an approach that deals with this problem. Furthermore, we present an idea to use our pattern mining approach to estimate the maintainability of modelled artifacts.