FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WAR: Weighted Association Rules for Item Intensities
Knowledge and Information Systems
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Scalable sequential pattern mining for biological sequences
Proceedings of the thirteenth ACM international conference on Information and knowledge management
TSP: Mining top-k closed sequential patterns
Knowledge and Information Systems
Binary Prediction Based on Weighted Sequential Mining Method
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
Data Mining and Knowledge Discovery
Mining minimal distinguishing subsequence patterns with gap constraints
Knowledge and Information Systems
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
A new framework for detecting weighted sequential patterns in large sequence databases
Knowledge-Based Systems
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Learning task models in ill-defined domain using an hybrid knowledge discovery framework
Knowledge-Based Systems
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
Effective next-items recommendation via personalized sequential pattern mining
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
A tree structure for event-based sequence mining
Knowledge-Based Systems
Mining generalized temporal patterns based on fuzzy counting
Expert Systems with Applications: An International Journal
Discovering forward sequences from temporal data
Knowledge-Based Systems
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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Sequential pattern mining, including weighted sequential pattern mining, has been attracting much attention since it is one of the essential data mining tasks with broad applications. The weighted sequential pattern mining aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. In the conventional weighted sequential pattern mining, usually pre-assigned weights of data elements are used to get the importance, which are derived from their quantitative information and their importance in real world application domains. In general sequential pattern mining, the generation order of data elements is considered to find sequential patterns. However, their generation times and time-intervals are also important in real world application domains. Therefore, time-interval information of data elements can be helpful in finding more interesting sequential patterns. This paper presents a new framework for finding time-interval weighted sequential (TiWS) patterns in a sequence database and time-interval weighted support (TiW-support) to find the TiWS patterns. In addition, a new method of mining TiWS patterns in a sequence database is also presented. In the proposed framework of TiWS pattern mining, the weight of each sequence in a sequence database is first obtained from the time-intervals of elements in the sequence, and subsequently TiWS patterns are found considering the weight. A series of evaluation results shows that TIWS pattern mining is efficient and helpful in finding more interesting sequential patterns.