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Parallelization of Sequential Pattern Sampling
2021 IEEE International Conference on Big Data (Big Data), 2021In the last years, the field of data mining has undergone extensive work on patterns discovery by sampling techniques. Recently, these sampling methods have been applied to sequential data that are complex in nature. The complexity of these data lies in their structure, which has a notable impact on the speed of the computation which is time consuming ...
Diop, Lamine, Ba, Cheikh
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Sequential Pattern Mining: Optimum Maximum Sequential Patterns and Consistent Sequential Patterns
2007 IEEE International Conference on Integration Technology, 2007The concepts of optimal maximum sequential pattern and the consistent sequential pattern are introduced to describe the wholesome characteristics of the sequential pattern mining problems. And a mathematical model and two algorithms are constructed to determine the optimal maximum sequential patterns and the consistent ones.
Xilu Wang, Weili Yao
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Proceedings of the Eleventh International Conference on Data Engineering, 2002
We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data.
Rakesh Agrawal 0001 +1 more
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We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data.
Rakesh Agrawal 0001 +1 more
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On a Method of Sequential Pattern Recognition
IEEE Transactions on Computers, 1972In this paper, a multistage linear programming method of pattern recognition is proposed. The usual n-dimensional linear program has been split up into n stages of a one-dimensional linear program in such a way that more and more patterns belonging to two classes A and B are correctly classified as we proceed to higher and higher stages.
A. Som, A. K. Nath
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Mining Compressing Sequential Patterns
Proceedings of the 2012 SIAM International Conference on Data Mining, 2012AbstractPattern mining based on data compression has been successfully applied in many data mining tasks. For itemset data, the Krimp algorithm based on the minimum description length (MDL) principle was shown to be very effective in solving the redundancy issue in descriptive pattern mining.
Lam, Hoang Thanh +3 more
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Sequential Pattern Mining with Wildcards
2010 22nd IEEE International Conference on Tools with Artificial Intelligence, 2010Sequential pattern mining is an important research task in many domains, such as biological science. In this paper, we study the problem of mining frequent patterns from sequences with wildcards. The user can specify the gap constraints with flexibility. Given a subject sequence, a minimal support threshold and a gap constraint, we aim to find frequent
Fei Xie 0002 +6 more
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Mining Sequential Patterns with Pattern Constraint
2015Mining sequential patterns is to find the sequential purchasing behaviors for most of the customers. There were many algorithms proposed for discovering all the sequential patterns. However, users may be only interested in certain items or behaviors.
Show-Jane Yen +3 more
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On Suboptimal Sequential Pattern Recognition
IEEE Transactions on Computers, 1968Abstract—In this note, three suboptimal solutions are obtained for the joint sequential feature selection and pattern classification problem. These solutions allow the comparison of two distinctly different approximations to the optimal procedure. One approximation involves simplifying assumptions on the underlying distribution of features for each ...
Gerald P. Cardillo, King-Sun Fu
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Mining sequential patterns for classification
Knowledge and Information Systems, 2015While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper,
Dmitriy Fradkin, Fabian Mörchen
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On progressive sequential pattern mining
Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, 2006When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. In practice, users are usually more interested in the recent data than the old ones.
Jen-Wei Huang +3 more
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