Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Opportunity map: a visualization framework for fast identification of actionable knowledge
Proceedings of the 14th ACM international conference on Information and knowledge management
Process support to help novices design software faster and better
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
WikiWiki weaving heterogeneous software artifacts
Proceedings of the 2005 international symposium on Wikis
A characterization of data mining algorithms on a modern processor
DaMoN '05 Proceedings of the 1st international workshop on Data management on new hardware
The role of the interaction designer in an agile software development process
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A View-Based Approach for Improving Software Documentation Practices
ECBS '06 Proceedings of the 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Testing to certify an embedded software system
Journal of Computing Sciences in Colleges
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Designing software is an expensive process. Our claim is that designing software can become more efficient if designers have knowledge about their design processes. One avenue of capturing the design process is through documentation. People have not focused on documentation for the purpose of generating design knowledge. A design platform called Lifecycle Manager (LCM) allows designers to document all steps of their design process. However, LCM does not have the means for interpreting the documentation that it collects. Our goal is to add that capability by studying how data mining can be applied to generate design knowledge. We developed a data mining tool and implemented four data mining algorithms on data captured by LCM, to learn that design knowledge cannot be gained from only one pattern. We learned about the evolution of LCM design by analyzing the data mining results. We plan to incorporate our tool into LCM.