Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Comparison of interestingness functions for learning web usage patterns
Proceedings of the eleventh international conference on Information and knowledge management
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
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
Managing Interesting Rules in Sequence Mining
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
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Industry: predicting telecommunication equipment failures from sequences of network alarms
Handbook of data mining and knowledge discovery
InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Binary Prediction Based on Weighted Sequential Mining Method
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Systems with Applications: An International Journal
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Non-redundant sequential rules-Theory and algorithm
Information Systems
An Intelligent Alarm Management System for Large-Scale Telecommunication Companies
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
GO-SPADE: mining sequential patterns over datasets with consecutive repetitions
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
CMRules: Mining sequential rules common to several sequences
Knowledge-Based Systems
ExMiner: an efficient algorithm for mining top-k frequent patterns
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Discovering frequent user--environment interactions in intelligent environments
Personal and Ubiquitous Computing
Statistical supports for frequent itemsets on data streams
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
An efficient approach to mine periodic-frequent patterns in transactional databases
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The time-interval between the antecedent and the consequent of a sequential rule can be considered as an important aspect in sequential rules interest. For example, in web logs analysis, the end-user can be interested in predicting the next page that will be visited by an internet surfer based on a history of visited pages. A Closeness Preference measure is proposed to favour the sequential rules with close itemsets based on user time-preference in a post-processing step. We illustrate the interest of the Closeness Preference measure with two real datasets (web logs data and activities of daily living data) for first, a predictive task and second, a descriptive one. Both of them show that Closeness Preference measure is helpful to find small and efficient sets of simple sequential rules.