TY - GEN
T1 - Interestingness measures for actionable patterns
AU - Tsay, Li Shiang
PY - 2014
Y1 - 2014
N2 - The ability to make mined patterns actionable is becoming increasingly important in today's competitive world. Standard data mining focuses on patterns that summarize data and these patterns are required to be further processed in order to determine opportunities for action. To address this problem, it is essential to extract patterns by comparing the profiles of two sets of relevant objects to obtain useful, understandable, and workable strategies. In this paper, we present the definition of actionable rules by integrating action rules and reclassification rules to build a framework for analyzing big data. In addition, three new interestingness measures, coverage, leverage, and lift, are proposed to address the limitations of minimum left support, right support and confidence thresholds for gauging the importance of discovered actionable rules.
AB - The ability to make mined patterns actionable is becoming increasingly important in today's competitive world. Standard data mining focuses on patterns that summarize data and these patterns are required to be further processed in order to determine opportunities for action. To address this problem, it is essential to extract patterns by comparing the profiles of two sets of relevant objects to obtain useful, understandable, and workable strategies. In this paper, we present the definition of actionable rules by integrating action rules and reclassification rules to build a framework for analyzing big data. In addition, three new interestingness measures, coverage, leverage, and lift, are proposed to address the limitations of minimum left support, right support and confidence thresholds for gauging the importance of discovered actionable rules.
KW - Action Rule
KW - Interestingness Measures
KW - Reclassification Model
KW - actionability
UR - https://www.scopus.com/pages/publications/84904813897
U2 - 10.1007/978-3-319-08729-0_27
DO - 10.1007/978-3-319-08729-0_27
M3 - Conference contribution
SN - 9783319087283
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 277
EP - 284
BT - Rough Sets and Intelligent Systems Paradigms - Second International Conference, RSEISP 2014, Held as Part of JRS 2014, Proceedings
PB - Springer Verlag
T2 - 2nd International Conference on Rough Sets and Emerging Intelligent Systems Paradigms, RSEISP 2014 - Held as Part of 2014 Joint Rough Set Symposium, JRS 2014
Y2 - 9 July 2014 through 13 July 2014
ER -