Results 271 to 280 of about 110,266 (316)
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Mining interestingness measures for string pattern mining
Knowledge-Based Systems, 2010A novel method of detecting interesting patterns in strings is presented. A common way to refine the results of pattern mining algorithms is by using interestingness measures. However, the set of appropriate measures differs for each domain and problem.
Manuel Baena-García +1 more
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From sequential pattern mining to structured pattern mining: A pattern-growth approach
Journal of Computer Science and Technology, 2004Sequential pattern mining is an important data mining problem with broad applications. However, it, is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Recent studies have developed two major classes of sequential pattern mining methods: (1) a candidate ...
Jiawei Han 0001 +2 more
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Mining frequent patterns with the pattern tree
New Generation Computing, 2005Mining frequent patterns with a frequent pattern tree (FP-tree in short) avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves much better performance and efficiency than Apriori-like algorithms. However, the database still needs to be scanned twice to get the FP-tree.
Hao Huang +2 more
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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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Distributed Convoy Pattern Mining
2016 17th IEEE International Conference on Mobile Data Management (MDM), 2016Due to the wide spread of mobile devices equipped with location sensors, the amount of mobility data being generated is enormous. Mining this data to reveal interesting behavioral patterns has gained attention in recent years. Various mobility patterns have been proposed which describe collective mobility behaviour.
Faisal Orakzai +2 more
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2016
This paper presents a new method to promote the performance of existing saliency detection algorithms. Prior bottom-up methods predict saliency maps by combining heuristic saliency cues, which may be unreliable. To remove error outputs and preserve accurate predictions, we develop a pattern mining based saliency seeds selection method.
Yuqiu Kong +4 more
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This paper presents a new method to promote the performance of existing saliency detection algorithms. Prior bottom-up methods predict saliency maps by combining heuristic saliency cues, which may be unreliable. To remove error outputs and preserve accurate predictions, we develop a pattern mining based saliency seeds selection method.
Yuqiu Kong +4 more
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Introduction to Pattern Mining
2014We present an overview of data mining techniques for extracting knowledge from large databases with a special emphasis on the unsupervised technique pattern mining. Pattern mining is often defined as the automatic search for interesting patterns and regularities in large databases.
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Novel Approach for Mining Patterns
International Journal of Applied Evolutionary Computation, 2021Process mining techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the work consists to mine small patterns from a log traces; those patterns are the representation of the traces execution from a log file ...
Ishak H. A. Meddah +2 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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Mining Surprising Periodic Patterns
Data Mining and Knowledge Discovery, 2004In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considered very significant if its actual occurrence frequency exceeds the prior expectation by a large margin.
Jiong Yang 0001 +2 more
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