AI Magazine
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multi-dimensional sequential pattern mining
Proceedings of the tenth international conference on Information and knowledge management
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
Feature selection on hierarchy of web documents
Decision Support Systems - Web retrieval and mining
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Efficient Mining of High Confidience Association Rules without Support Thresholds
PKDD '99 Proceedings of the Third 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
Quality assessment of knowledge-based software: some certification considerations
ISESS '97 Proceedings of the 3rd International Software Engineering Standards Symposium (ISESS '97)
Web-Log Mining for Predictive Web Caching
IEEE Transactions on Knowledge and Data Engineering
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
An introduction to variable and feature selection
The Journal of Machine Learning Research
A predictive location model for location-based services
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
The Role of the Management Sciences in Research on Personalization
Management Science
Building Association-Rule Based Sequential Classifiers for Web-Document Prediction
Data Mining and Knowledge Discovery
Location Based Services
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Detection of Significant Sets of Episodes in Event Sequences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns from Large Data Sets (The Kluwer International Series on Advances in Database Systems)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis
IEEE Transactions on Knowledge and Data Engineering
Grouping Multidimensional Data: Recent Advances in Clustering
Grouping Multidimensional Data: Recent Advances in Clustering
\delta-Tolerance Closed Frequent Itemsets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Algorithms on Strings
A review for mobile commerce research and applications
Decision Support Systems
Personalized local internet in the location-based mobile web search
Decision Support Systems
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Effective elimination of redundant association rules
Data Mining and Knowledge Discovery
A survey on context-aware systems
International Journal of Ad Hoc and Ubiquitous Computing
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A framework for context sensitive services: A knowledge discovery based approach
Decision Support Systems
Expert Systems with Applications: An International Journal
Redundant association rules reduction techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
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Personalized marketing via mobile devices, also known as Mobile Personalized Marketing (MPM), has become an increasingly important marketing tool because the ubiquity, interactivity and localization of mobile devices offers great potential for understanding customers' preferences and quickly advertising customized products or services. A tremendous challenge in MPM is to factor a mobile user's context into the prediction of the user's preferences. This paper proposes a novel framework with a three-stage procedure to discover the correlation between contexts of mobile users and their activities for better predicting customers' preferences. Our framework helps not only to discover sequential rules from contextual data, but also to overcome a common barrier in mining contextual data, i.e. elimination of redundant rules that occur when multiple dimensions of contextual information are used in the prediction. The effectiveness of our framework is evaluated through experiments conducted on a mobile user's context dataset. The results show that our framework can effectively extract patterns from a mobile customer's context information for improving the prediction of his/her activities.