C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A microeconomic data mining problem: customer-oriented catalog segmentation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Mining Patterns That Respond to Actions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
Mining long high utility itemsets in transaction databases
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
A Profit-Based Business Model for Evaluating Rule Interestingness
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
On domination game analysis for microeconomic data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
Towards Business Interestingness in Actionable Knowledge Discovery
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Mining action rules from scratch
Expert Systems with Applications: An International Journal
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
New prediction model for pre-fetching in mobile database
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
DualRank: a dual-phase algorithm for optimal profit mining in retailing market
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Incorporating frequency, recency and profit in sequential pattern based recommender systems
Intelligent Data Analysis
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A major obstacle in data mining applications is the gap between the statistic-based pattern extraction and the value-based decision making. We present a profit mining approach to reduce this gap. In profit mining, we are given a set of past transactions and pre-selected target items, and we like to build a model for recommending target items and promotion strategies to new customers, with the goal of maximizing the net profit. We identify several issues in profit mining and propose solutions. We evaluate the effectiveness of this approach using data sets of a wide range of characteristics.