Fuzzy sets, decision making and expert systems
Fuzzy sets, decision making and expert systems
On nearness measures in fuzzy relational data models
International Journal of Approximate Reasoning
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Quantifying the utility of the past in mining large databases
Information Systems
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Working Knowledge: How Organizations Manage What They Know
Working Knowledge: How Organizations Manage What They Know
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
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
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
A fuzzy data mining algorithm for finding sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Refinement of temporal constraints in fuzzy associations
International Journal of Approximate Reasoning
Mining fuzzy temporal patterns from process instances with weighted temporal graphs
International Journal of Data Analysis Techniques and Strategies
An ACS-based framework for fuzzy data mining
Expert Systems with Applications: An International Journal
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
SART: a new association rule method for mining sequential patterns in time series of climate data
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
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In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.