Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Neural network applications in business: a review and analysis of the literature (1988-95)
Decision Support Systems
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Machine Learning
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Quantifiable data mining using ratio rules
The VLDB Journal — The International Journal on Very Large Data Bases
Building an Association Rules Framework to Improve Product Assortment Decisions
Data Mining and Knowledge Discovery
On the complexity of inducing categorical and quantitative association rules
Theoretical Computer Science
Enhancing Product Recommender Systems on Sparse Binary Data
Data Mining and Knowledge Discovery
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
On strategies for imbalanced text classification using SVM: A comparative study
Decision Support Systems
Analyzing Price Data to Determine Positive and Negative Product Associations
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Re-mining positive and negative association mining results
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A framework for visualizing association mining results
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Expert Systems with Applications: An International Journal
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Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.