The Experts below are selected from a list of 5253 Experts worldwide ranked by ideXlab platform
Jia Wei Han - One of the best experts on this subject based on the ideXlab platform.
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Sequential Pattern Mining
Frequent Pattern Mining, 2014Co-Authors: Wei Shen, Jianyong Wang, Jia Wei HanAbstract: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. However, it is also a challenging problem since the Mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Abundant literature has been dedicated to this research and tremendous progress has been made so far. This chapter will present a thorough overview and analysis of the main approaches to Sequential Pattern Mining.
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Constraint-based Sequential Pattern Mining: The Pattern-growth methods
Journal of Intelligent Information Systems, 2007Co-Authors: Jian Pei, Jia Wei Han, Wei WangAbstract:Abstract Constraints are essential for many Sequential Pattern Mining applications. However, there is no systematic study on constraint-based Sequential Pattern Mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-Pattern Mining\ndoes not fit our mission well. An extended framework is developed based on a Sequential Pattern growth methodology. Our study\nshows that constraints can be effectively and efficiently pushed deep into the Sequential Pattern Mining under this new framework.\nMoreover, this framework can be extended to constraint-based structured Pattern Mining as well.
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Sequential Pattern Mining by Pattern-Growth: Principles and Extensions
2005Co-Authors: Jia Wei Han, Jian Pei, Xifeng YanAbstract:Sequential 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 generation-and-test approach, represented by (i) GSP [30], a horizontal format-based Sequential Pattern Mining method, and (ii) SPADE [36], a vertical format-based method; and (2) a Sequential Pattern growth method, represented by PrefixSpan [26] and its further extensions, such as CloSpan for Mining closed Sequential Patterns [35]. In this study, we perform a systematic introduction and presentation of the Pattern-growth methodology and study its principles and extensions. We first introduce two interesting Pattern growth algorithms, FreeSpan [11] and PrefixSpan [26], for efficient Sequential Pattern Mining. Then we introduce CloSpan for Mining closed Sequential Patterns. Their relative performance in large sequence databases is presented and analyzed. The various kinds of extension of these methods for (1) Mining constraint-based Sequential Patterns, (2) Mining multi-level, multi-dimensional Sequential Patterns, (3) Mining top-k closed Sequential Patterns, and (4) their applications in bio-sequence Pattern analysis and clustering sequences are also discussed in the paper.
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From Sequential Pattern Mining to structured Pattern Mining: A Pattern-growth approach
Journal of Computer Science and Technology, 2004Co-Authors: Jia Wei Han, Jian Pei, Xifeng YanAbstract:Sequential 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 generation-and-test approach, represented by (i) GSP, a horizontal format-based Sequential Pattern Mining method, and (ii) SPADE, a vertical format-based method; and (2) a Pattern-growth method, represented by Pre xSpan and its further extensions, such as gSpan for Mining structured Patterns. In this study, we perform a systematic introduction and presentation of the Pattern-growth methodology and study its principles and extensions. We rst introduce two interesting Pattern-growth algorithms, FreeSpan and Pre xSpan, for eÆcient Sequential Pattern Mining. Then we introduce gSpan for Mining structured Patterns using the same methodology. Their relative performance in large databases is presented and analyzed. Several extensions of these methods are also discussed in the paper, including Mining multi-level, multi-dimensional Patterns and Mining constraint-based Patterns.
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Multi-dimensional Sequential Pattern Mining
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01, 2001Co-Authors: Helen Pinto, Jia Wei Han, Qiming Chen, Ke Wang, Jian Pei, Umeshwar DayalAbstract:Sequential Pattern Mining, which finds the set of frequent subsequences in sequence databases, is an important data-Mining task and has broad applications. Usually, sequence Patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine Sequential Patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional Sequential Pattern Mining, which integrates the multidimensional analysis and Sequential data Mining. We also thoroughly explore efficient methods for multi-dimensional Sequential Pattern Mining. We examine feasible combinations of efficient Sequential Pattern Mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance Mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
Zhu Jianqiu - One of the best experts on this subject based on the ideXlab platform.
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Time-enriched Sequential Pattern Mining Algorithm TESP
Computer Engineering, 2004Co-Authors: Zhu JianqiuAbstract:In this paper, the time-enriched Sequential Pattern concept is introduced, and a novel Mining algorithm, called TESP(time-enriched Sequential Pattern Mining ), is developed, which also enables users to issue many time focused constraints and enhances flexibility and usefulness of Sequential Patterns Mining.
Jian Pei - One of the best experts on this subject based on the ideXlab platform.
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Constraint-based Sequential Pattern Mining: The Pattern-growth methods
Journal of Intelligent Information Systems, 2007Co-Authors: Jian Pei, Jia Wei Han, Wei WangAbstract:Abstract Constraints are essential for many Sequential Pattern Mining applications. However, there is no systematic study on constraint-based Sequential Pattern Mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-Pattern Mining\ndoes not fit our mission well. An extended framework is developed based on a Sequential Pattern growth methodology. Our study\nshows that constraints can be effectively and efficiently pushed deep into the Sequential Pattern Mining under this new framework.\nMoreover, this framework can be extended to constraint-based structured Pattern Mining as well.
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Sequential Pattern Mining by Pattern-Growth: Principles and Extensions
2005Co-Authors: Jia Wei Han, Jian Pei, Xifeng YanAbstract:Sequential 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 generation-and-test approach, represented by (i) GSP [30], a horizontal format-based Sequential Pattern Mining method, and (ii) SPADE [36], a vertical format-based method; and (2) a Sequential Pattern growth method, represented by PrefixSpan [26] and its further extensions, such as CloSpan for Mining closed Sequential Patterns [35]. In this study, we perform a systematic introduction and presentation of the Pattern-growth methodology and study its principles and extensions. We first introduce two interesting Pattern growth algorithms, FreeSpan [11] and PrefixSpan [26], for efficient Sequential Pattern Mining. Then we introduce CloSpan for Mining closed Sequential Patterns. Their relative performance in large sequence databases is presented and analyzed. The various kinds of extension of these methods for (1) Mining constraint-based Sequential Patterns, (2) Mining multi-level, multi-dimensional Sequential Patterns, (3) Mining top-k closed Sequential Patterns, and (4) their applications in bio-sequence Pattern analysis and clustering sequences are also discussed in the paper.
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From Sequential Pattern Mining to structured Pattern Mining: A Pattern-growth approach
Journal of Computer Science and Technology, 2004Co-Authors: Jia Wei Han, Jian Pei, Xifeng YanAbstract:Sequential 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 generation-and-test approach, represented by (i) GSP, a horizontal format-based Sequential Pattern Mining method, and (ii) SPADE, a vertical format-based method; and (2) a Pattern-growth method, represented by Pre xSpan and its further extensions, such as gSpan for Mining structured Patterns. In this study, we perform a systematic introduction and presentation of the Pattern-growth methodology and study its principles and extensions. We rst introduce two interesting Pattern-growth algorithms, FreeSpan and Pre xSpan, for eÆcient Sequential Pattern Mining. Then we introduce gSpan for Mining structured Patterns using the same methodology. Their relative performance in large databases is presented and analyzed. Several extensions of these methods are also discussed in the paper, including Mining multi-level, multi-dimensional Patterns and Mining constraint-based Patterns.
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Multi-dimensional Sequential Pattern Mining
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01, 2001Co-Authors: Helen Pinto, Jia Wei Han, Qiming Chen, Ke Wang, Jian Pei, Umeshwar DayalAbstract:Sequential Pattern Mining, which finds the set of frequent subsequences in sequence databases, is an important data-Mining task and has broad applications. Usually, sequence Patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine Sequential Patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional Sequential Pattern Mining, which integrates the multidimensional analysis and Sequential data Mining. We also thoroughly explore efficient methods for multi-dimensional Sequential Pattern Mining. We examine feasible combinations of efficient Sequential Pattern Mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance Mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
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CIKM - Multi-dimensional Sequential Pattern Mining
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01, 2001Co-Authors: Helen Pinto, Jia Wei Han, Qiming Chen, Ke Wang, Jian Pei, Umeshwar DayalAbstract:Sequential Pattern Mining, which finds the set of frequent subsequences in sequence databases, is an important data-Mining task and has broad applications. Usually, sequence Patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine Sequential Patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional Sequential Pattern Mining, which integrates the multidimensional analysis and Sequential data Mining. We also thoroughly explore efficient methods for multi-dimensional Sequential Pattern Mining. We examine feasible combinations of efficient Sequential Pattern Mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance Mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
Umeshwar Dayal - One of the best experts on this subject based on the ideXlab platform.
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Multi-dimensional Sequential Pattern Mining
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01, 2001Co-Authors: Helen Pinto, Jia Wei Han, Qiming Chen, Ke Wang, Jian Pei, Umeshwar DayalAbstract:Sequential Pattern Mining, which finds the set of frequent subsequences in sequence databases, is an important data-Mining task and has broad applications. Usually, sequence Patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine Sequential Patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional Sequential Pattern Mining, which integrates the multidimensional analysis and Sequential data Mining. We also thoroughly explore efficient methods for multi-dimensional Sequential Pattern Mining. We examine feasible combinations of efficient Sequential Pattern Mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance Mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
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CIKM - Multi-dimensional Sequential Pattern Mining
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01, 2001Co-Authors: Helen Pinto, Jia Wei Han, Qiming Chen, Ke Wang, Jian Pei, Umeshwar DayalAbstract:Sequential Pattern Mining, which finds the set of frequent subsequences in sequence databases, is an important data-Mining task and has broad applications. Usually, sequence Patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine Sequential Patterns associated with multi-dimensional information.In this paper, we propose the theme of multi-dimensional Sequential Pattern Mining, which integrates the multidimensional analysis and Sequential data Mining. We also thoroughly explore efficient methods for multi-dimensional Sequential Pattern Mining. We examine feasible combinations of efficient Sequential Pattern Mining and multi-dimensional analysis methods, as well as develop uniform methods for high-performance Mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.
Wanli Zuo - One of the best experts on this subject based on the ideXlab platform.
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Multi-dimensional Sequential Pattern Mining Based on Concept Lattice
Lecture Notes in Computer Science, 2006Co-Authors: Yang Jin, Wanli ZuoAbstract:Multi-dimensional Sequential Pattern Mining attempts to find much more informative frequent Patterns suitable for immediate use. In this paper, a novel data model called multi-dimensional concept lattice is proposed and, based on which, a new incremental multi-dimensional Sequential Pattern Mining algorithm is developed. The proposed algorithm integrates Sequential Pattern Mining and association Pattern Mining with a uniform data structure and makes the Mining process more efficient. The performance of the proposed approach is evaluated on both synthetic and real-life financial date sets.
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ADMA - Multi-dimensional Sequential Pattern Mining based on concept lattice
Advanced Data Mining and Applications, 2006Co-Authors: Yang Jin, Wanli ZuoAbstract:Multi-dimensional Sequential Pattern Mining attempts to find much more informative frequent Patterns suitable for immediate use. In this paper, a novel data model called multi-dimensional concept lattice is proposed and, based on which, a new incremental multi-dimensional Sequential Pattern Mining algorithm is developed. The proposed algorithm integrates Sequential Pattern Mining and association Pattern Mining with a uniform data structure and makes the Mining process more efficient. The performance of the proposed approach is evaluated on both synthetic and real-life financial date sets.
