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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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Trajectory Data Pattern Mining
2014In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns.
Masciari E, Shi Gao, Carlo Zaniolo
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Mining mutually dependent patterns
Proceedings 2001 IEEE International Conference on Data Mining, 2002In some domains, such as isolating problems in computer networks and discovering stock market irregularities, there is more interest in patterns consisting of infrequent, but highly correlated items rather than patterns that occur frequently (as defined by minsup, the minimum support level).
Sheng Ma, Joseph L. Hellerstein
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2021
Bi-pattern mining has been previously introduced to mine attributed networks in which nodes may have two types or two roles. In particular a bi-partite network have two vertex sets and attributes describing node labels depends then on the node type, still some attributes may be relevant to describe both kind of nodes.
Guillaume Santini +2 more
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Bi-pattern mining has been previously introduced to mine attributed networks in which nodes may have two types or two roles. In particular a bi-partite network have two vertex sets and attributes describing node labels depends then on the node type, still some attributes may be relevant to describe both kind of nodes.
Guillaume Santini +2 more
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Issues in pattern mining and their resolutions
Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering, 2009Frequent pattern mining over large database is fundamental to many data mining applications. Various approaches have been proposed for pattern mining with respectable computational performance. However, our study has found some fundamental problems, such as overfitting and probability anomaly, which have not been well addressed.
Tongyuan Wang, Bipin C. Desai
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Sequence Pattern Mining with Variables
IEEE Transactions on Knowledge and Data Engineering, 2020Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that the relevant differences between items are captured through creating distinct items. In some domains, this leads to an exponential increase in the number of items.
James S. Okolica +3 more
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Probabilistic Frequent Pattern Mining by PUH-Mine
2015To mine frequent itemsets from uncertain data, many existing algorithms rely on expected support based mining. An alternative approach relies on probabilistic based mining, which captures the frequentness probability. While the possible world semantics are widely used, the exponential growth of possible worlds makes the probabilistic based mining ...
Wenzhu Tong +3 more
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2012 IIAI International Conference on Advanced Applied Informatics, 2012
We herein investigate finding unusual patterns from a given string as a text. In the present paper, the pattern is expressed as a sub string of the string. The natural assumption with respect to the frequency of a pattern is that the shorter the length of the pattern, the larger the frequency of the pattern.
Yasuhiro Yamada +3 more
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We herein investigate finding unusual patterns from a given string as a text. In the present paper, the pattern is expressed as a sub string of the string. The natural assumption with respect to the frequency of a pattern is that the shorter the length of the pattern, the larger the frequency of the pattern.
Yasuhiro Yamada +3 more
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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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Mining inter-sequence patterns
Expert Systems with Applications, 2009Sequential pattern and inter-transaction pattern mining have long been important issues in data mining research. The former finds sequential patterns without considering the relationships between transactions in databases, while the latter finds inter-transaction patterns without considering the ordered relationships of items within each transaction ...
Chun-Sheng Wang, Anthony J. T. Lee
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