Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Introduction to Digital Logic Design
Introduction to Digital Logic Design
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficiently Mining Frequent Closed Partial Orders
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Towards generic pattern mining
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
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Data mining and sequential mining analysis are considered as crucial components of strategic control over a broad variety of disciplines in business, science and engineering. Data mining has been defined as the non- trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered. In Sequence mining, data are represented as sequences of events, where order of those events is important. Finding patterns in sequences is valuable for predicting future events. In many applications such as the WEB applications, stock market, and genetic analysis, finding patterns in a sequence of elements or events, helps in predicting what could be the next event or element. At the conceptual level, association mining and sequence mining are two similar processes but using different representations of data. In association mining, items are distinct and the order of items in a transaction is not important. While in sequential pattern mining, the order of elements (events) in transactions (sequences) is important, and the same event may occur more than once. In this paper, we propose a new mapping function that maps event sequences into itemsets. Based on the unified representation of the association mining and the sequential pattern, a new approach that uses the Boolean representation of input database D to build a Boolean matrix M. Boolean algebra operations are applied on M to generate all frequent itemsets. Finally, frequent items or frequent sequential patterns are represented by logical expressions that could be minimized by using a suitable logical function minimization technique.