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
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
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
An efficient mining of weighted frequent patterns with length decreasing support constraints
Knowledge-Based Systems
Mining Weighted Closed Sequential Patterns in Large Databases
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 05
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A data mining approach to discovering reliable sequential patterns
Journal of Systems and Software
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
| Hi-index | 12.05 |
Unlike the general sequential pattern mining that considers only the generation order of data elements, mining weighted sequential patterns aims to get more interesting sequential patterns by considering the weights of data elements in a target sequence database in addition to their generation order. In general, for a sequence or a sequential pattern, not only the generation order of data elements but also their generation times and time-intervals are important because they can be helpful in finding more interesting sequential patterns. Applying the mining method of time-interval weighted sequential (TiWS) patterns that has been proposed in our previous work, this paper proposes several sequence weighting approaches to get the time-interval weight of a sequence in mining TiWS patterns for a sequence database, and the effectiveness of each approach in mining TiWS patterns is analyzed through a set of experiments. The proposed sequence weighting approaches may be helpful in obtaining more interesting sequential patterns in mining sequential patterns for a sequence database.