Knowledge discovery in databases: an overview
AI Magazine
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Feature Interaction and Dependencies: Modeling Features for Reengineering a Legacy Product Line
SPLC 2 Proceedings of the Second International Conference on Software Product Lines
Variability Issues in Software Product Lines
PFE '01 Revised Papers from the 4th International Workshop on Software Product-Family Engineering
Mining Version Histories to Guide Software Changes
Proceedings of the 26th International Conference on Software Engineering
Optimization of Variability in Software Product Lines
SPLC '07 Proceedings of the 11th International Software Product Line Conference
Automated error analysis for the agilization of feature modeling
Journal of Systems and Software
Decision-Model-Based Code Generation for SPLE
SPLC '08 Proceedings of the 2008 12th International Software Product Line Conference
A method to identify feature constraints based on feature selections mining
SPLC'10 Proceedings of the 14th international conference on Software product lines: going beyond
Towards consistent evolution of feature models
SPLC'10 Proceedings of the 14th international conference on Software product lines: going beyond
Usage scenarios for feature model synthesis
Proceedings of the VARiability for You Workshop: Variability Modeling Made Useful for Everyone
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Feature selections mining is the process of discovering potentially feature associations and constraints in data. Especially, mining from time-series data obtains feature constraint trends. In this paper, we describe an approach to evaluate feature constraint trends and present results of two case studies. Feature selections mining was applied to a product transactions database at Hitachi. The product transactions had 148 optional features, and 8,372 products were derived from the product line. Both case studies focus on transaction-time periods: time series and time intervals. Feature selections mining discovered feature constraints around 100 rules in each study, and determined they constantly change.