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
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Net worth: shaping markets when customers make the rules
Net worth: shaping markets when customers make the rules
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
User profiling in personalization applications through rule discovery and validation
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Workshop on recommender systems: algorithms and evaluation
ACM SIGIR Forum
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
Internet World Guide to One-to-One Web Marketing
Internet World Guide to One-to-One Web Marketing
Data Mining and Knowledge Discovery
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Interacting with recommender systems
CHI '99 Extended Abstracts on Human Factors in Computing Systems
Abstract-Driven Pattern Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Fast algorithms for mining association rules and sequential patterns
Fast algorithms for mining association rules and sequential patterns
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Objective and Subjective Algorithms for Grouping Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning User Similarity and Rating Style for Collaborative Recommendation
Information Retrieval
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Event sequence mining to develop profiles for computer forensic investigation purposes
ACSW Frontiers '06 Proceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 54
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
IEEE Transactions on Knowledge and Data Engineering
Customer-centric marketing with Internet coupons
Decision Support Systems
Designing evolving user profile in e-CRM with dynamic clustering of Web documents
Data & Knowledge Engineering
A collaborative constraint-based meta-level recommender
Proceedings of the 2008 ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
Measuring similarity in feature space of knowledge entailed by two separate rule sets
Knowledge-Based Systems
Testing terrorism theory with data mining
International Journal of Data Analysis Techniques and Strategies
Culturally adaptive software: moving beyond internationalization
UI-HCII'07 Proceedings of the 2nd international conference on Usability and internationalization
Web user behavioral profiling for user identification
Decision Support Systems
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Product matching algorithm for cooperative commerce model
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Investigative behavior profiling with one class SVM for computer forensics
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Exploring fuzzy ontologies in mining generalized association rules
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Discovering patterns of online purchasing behaviour and a new-product-launch strategy
Expert Systems: The Journal of Knowledge Engineering
An iterative approach to build relevant ontology-aware data-driven models
Information Sciences: an International Journal
Advance missing data processing for collaborative filtering
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
International Journal of Business Information Systems
An improved neighborhood-restricted association rule-based recommender system
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
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In many e-commerce applications, ranging from dynamic Web content presentation, to personalized ad targeting, to individual recommendations to the customers, it is important to build personalized profiles of individual users from their transactional histories. These profiles constitute models of individual user behavior and can be specified with sets of rules learned from user transactional histories using various data mining techniques. Since many discovered rules can be spurious, irrelevant, or trivial, one of the main problems is how to perform post-analysis of the discovered rules, i.e., how to validate user profiles by separating “good” rules from the “bad.” This validation process should be done with an explicit participation of the human expert. However, complications may arise because there can be very large numbers of rules discovered in the applications that deal with many users, and the expert cannot perform the validation on a rule-by-rule basis in a reasonable period of time. This paper presents a framework for building behavioral profiles of individual users. It also introduces a new approach to expert-driven validation of a very large number of rules pertaining to these users. In particular, it presents several types of validation operators, including rule grouping, filtering, browsing, and redundant rule elimination operators, that allow a human expert validate many individual rules at a time. By iteratively applying such operators, the human expert can validate a significant part of all the initially discovered rules in an acceptable time period. These validation operators were implemented as a part of a one-to-one profiling system. The paper also presents a case study of using this system for validating individual user rules discovered in a marketing application.