Introduction to algorithms
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Mining fuzzy association rules in databases
ACM SIGMOD Record
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 hybrid sequential patterns and sequential rules
Information Systems
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Classification and Relationship Extraction Scheme for Raltional Databases Based on Fuzzy Logic
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
SQUIRE: Sequential Pattern Mining with Quantities
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
A Method for Generation of Alternatives by Decision Support Systems
Journal of Management Information Systems
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
Recognizing unexpected recurrence behaviors with fuzzy measures in sequence databases
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Knowledge gathering of fuzzy multi-time-interval sequential patterns
Information Sciences: an International Journal
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
International Journal of Automation and Computing
Information Sciences: an International Journal
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
| Hi-index | 0.20 |
Given a sequence database and minimum support threshold, the goal of mining quantitative sequential patterns is to discover the complete set of sequential patterns with purchased quantities in databases. Although this type of pattern can provide more information than the traditional sequential pattern, it also causes a sharp boundary problem. This means that when an item's quantity is close to the boundary of two adjacent quantity intervals, it is either ignored or overemphasized. In view of this weakness, a recent paper from Hong, Kuo, and Chi proposed a new kind of extended patterns, called fuzzy quantitative sequential patterns (FQSP), where an item's quantity in the pattern is represented by a fuzzy term rather than a quantity interval. In their work an Apriori-like algorithm was developed to mine all FQSP. In this paper, we propose a new and novel algorithm to mine FQSP based on the divide-and-conquer strategy. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is much more efficient and scalable than the previous algorithm.