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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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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 ...
Jia-Wei Han, Jian Pei, Xi-Feng Yan
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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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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.
R. Agrawal, R. Srikant
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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.
R. Agrawal, R. Srikant
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2020
In this chapter, we first look at patterns with their relevance of discovery to business. We then do a survey and evaluation, in terms of advantages and disadvantages, of different mining algorithms that are suited for both traditional and big data sources. These algorithms include those designed for both sequential and closed sequential pattern mining
Richard Millham +2 more
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In this chapter, we first look at patterns with their relevance of discovery to business. We then do a survey and evaluation, in terms of advantages and disadvantages, of different mining algorithms that are suited for both traditional and big data sources. These algorithms include those designed for both sequential and closed sequential pattern mining
Richard Millham +2 more
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2014
Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, has been a focused theme in data mining research for over a decade. This problem has broad applications, such as mining customer purchase patterns and Web access patterns.
Wei Shen, Jianyong Wang, Jiawei Han
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Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, has been a focused theme in data mining research for over a decade. This problem has broad applications, such as mining customer purchase patterns and Web access patterns.
Wei Shen, Jianyong Wang, Jiawei Han
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2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018
’Data mining’ itself explains that it is the mining of data from large reserve of data generated every day. Business uses data mining to make important decisions to increase their revenue, increase the potential customer base, reduce costs etc. In the retail industry, useful pattern discovery is essential out of huge amount of data is generated ...
Ashmita Saha, Vaishali D. Khairnar
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’Data mining’ itself explains that it is the mining of data from large reserve of data generated every day. Business uses data mining to make important decisions to increase their revenue, increase the potential customer base, reduce costs etc. In the retail industry, useful pattern discovery is essential out of huge amount of data is generated ...
Ashmita Saha, Vaishali D. Khairnar
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2010
Graph pattern mining becomes increasingly crucial to applications in a variety of domains including bioinformatics, cheminformatics, social network analysis, computer vision and multimedia. In this chapter, we first examine the existing frequent subgraph mining algorithms and discuss their computational bottleneck.
Hong Cheng, Xifeng Yan, Jiawei Han
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Graph pattern mining becomes increasingly crucial to applications in a variety of domains including bioinformatics, cheminformatics, social network analysis, computer vision and multimedia. In this chapter, we first examine the existing frequent subgraph mining algorithms and discuss their computational bottleneck.
Hong Cheng, Xifeng Yan, Jiawei Han
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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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2009
In this chapter, we introduce a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. This process is called Sequence Pattern Mining.
Huiyu Zhou +3 more
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In this chapter, we introduce a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. This process is called Sequence Pattern Mining.
Huiyu Zhou +3 more
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