Abstract
Association rule mining discovers patterns of co-occurrences of attributes as association rules in a data set. The derived association rules are expected to be recurrent, that is, the patterns recur in future in other data sets. This paper defines the recurrence of a rule, and aims to find a criteria to distinguish between high recurrent rules and low recurrent ones using a data set for software defect prediction. An experiment with the Eclipse Mylyn defect data set showed that rules of lower than 30 transactions showed low recurrence. We also found that the lower bound of transactions to select high recurrence rules is dependent on the required precision of defect prediction.
| Original language | English |
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| Title of host publication | 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509008063 |
| DOIs | |
| Publication status | Published - Aug 23 2016 |
| Event | 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, Japan Duration: Jun 26 2016 → Jun 29 2016 |
Other
| Other | 15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 |
|---|---|
| Country/Territory | Japan |
| City | Okayama |
| Period | 6/26/16 → 6/29/16 |
Keywords
- association rule mining
- data mining
- defect prediction
- empirical study
- software quality
ASJC Scopus subject areas
- Computer Science(all)
- Energy Engineering and Power Technology
- Control and Optimization