Applications of Pattern Discovery Using Sequential Data Mining

Data Mining ◽

10.4018/978-1-4666-2455-9.ch048 ◽

2013 ◽

pp. 947-969

Author(s):

Manish Gupta ◽

Jiawei Han

Keyword(s):

Data Mining ◽

Text Mining ◽

Intrusion Detection ◽

Pattern Mining ◽

Pattern Discovery ◽

Sequential Pattern Mining ◽

Web Usage Mining ◽

Sequential Pattern ◽

Sequential Data ◽

Mining Methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

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Multi-Relational Sequential Pattern Mining Based on Iceberg Concept Lattice

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.109.729 ◽

2011 ◽

Vol 109 ◽

pp. 729-733

Author(s):

Jiang Yin ◽

Yun Li ◽

Cen Cheng Shen ◽

Bo Liu

Keyword(s):

Data Mining ◽

Time Complexity ◽

Pattern Mining ◽

Concept Lattice ◽

Optimization Methods ◽

Sequential Pattern Mining ◽

Experimental Results ◽

Sequential Pattern ◽

Sequential Mining ◽

Mining Methods

Multi-Relational Sequential mining is one of the areas of data mining that rapidly developed in recent years. However, the performance issues of traditional mining methods are not ideal. To effectively mining the pattern, we proposed an algorithm based on Iceberg concept lattice, adopting optimization methods of partition and merger to just mining the frequent sequences. Experimental results show this algorithm effectively reduced the time complexity of multi-relational sequential pattern mining.

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Approaches for Pattern Discovery Using Sequential Data Mining

Data Mining ◽

10.4018/978-1-4666-2455-9.ch095 ◽

2013 ◽

pp. 1835-1851

Author(s):

Manish Gupta ◽

Jiawei Han

Keyword(s):

Data Mining ◽

Pattern Mining ◽

Sequence Data ◽

Sequential Pattern Mining ◽

Sequential Pattern ◽

Sequential Data ◽

Stream Data ◽

Dual Representation ◽

Advantages And Disadvantages ◽

Growth Methods

In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the most influential methods for sequential pattern mining. There is also a vertical format based method which works on a dual representation of the sequence database. Work has also been done for mining patterns with constraints, mining closed patterns, mining patterns from multi-dimensional databases, mining closed repetitive gapped subsequences, and other forms of sequential pattern mining. Some works also focus on mining incremental patterns and mining from stream data. We present at least one method of each of these types and discuss their advantages and disadvantages. We conclude with a summary of the work.

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A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1448 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1634-1639

Author(s):

V Aruna, Et. al.

Keyword(s):

Web Mining ◽

Pattern Mining ◽

Pattern Discovery ◽

Relevant Information ◽

Sequential Pattern Mining ◽

Web Usage Mining ◽

Sequential Pattern ◽

Web Usage ◽

Hidden Data ◽

The Web

In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge present in the Web log files , in recognizing various interests of web users and also in discovering customer behaviours. Web Usage mining includes different phases of data mining techniques called Data Pre-processing, Pattern Discovery & Pattern Analysis. This paper presents an updated focused survey on various sequential pattern mining algorithms like apriori-based algorithm , Breadth First Search-based strategy, Depth First Search strategy, sequential closed-pattern algorithm and Incremental pattern mining algorithm which are used in Pattern Discovery Phase of WUM. At last , a comparison is done based on the important key features present in these algorithms. This study gives us better understanding of the approaches of sequential pattern mining.

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Approaches for Pattern Discovery Using Sequential Data Mining

Pattern Discovery Using Sequence Data Mining ◽

10.4018/978-1-61350-056-9.ch008 ◽

2012 ◽

pp. 137-154 ◽

Cited By ~ 4

Author(s):

Manish Gupta ◽

Jiawei Han

Keyword(s):

Data Mining ◽

Pattern Mining ◽

Sequence Data ◽

Sequential Pattern Mining ◽

Sequential Pattern ◽

Sequential Data ◽

Stream Data ◽

Dual Representation ◽

Advantages And Disadvantages ◽

Growth Methods

In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the most influential methods for sequential pattern mining. There is also a vertical format based method which works on a dual representation of the sequence database. Work has also been done for mining patterns with constraints, mining closed patterns, mining patterns from multi-dimensional databases, mining closed repetitive gapped subsequences, and other forms of sequential pattern mining. Some works also focus on mining incremental patterns and mining from stream data. We present at least one method of each of these types and discuss their advantages and disadvantages. We conclude with a summary of the work.

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SEQUENTIAL PATTERN MINING AND NONMONOTONIC REASONING FOR INTELLIGENT INFORMATION AGENTS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005673 ◽

2007 ◽

Vol 21 (04) ◽

pp. 773-789 ◽

Cited By ~ 2

Author(s):

RAYMOND Y. K. LAU ◽

YUEFENG LI ◽

SHENG-TANG WU ◽

XUJUAN ZHOU

Keyword(s):

Data Mining ◽

Information Retrieval ◽

Belief Revision ◽

Pattern Mining ◽

Sequential Pattern Mining ◽

Sequential Pattern ◽

Sequential Data ◽

Contextual Knowledge ◽

Information Agents ◽

Intelligent Information

With the explosive growth of information available on the Internet, more effective data mining and data reasoning mechanism is required to process the sheer volume of information. Belief revision logic offers the expressive power to represent information retrieval contexts, and it also provides a sound inference mechanism to model the nonmonotonicity arising in changing retrieval contexts. Contextual knowledge for information retrieval can be extracted via efficient sequential pattern mining. We present a pattern taxonomy extraction model which efficiently performs the task of discovering descriptive frequent sequential patterns by pruning the noisy associations. This paper illustrates a novel approach of integrating the sequential data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Initial experiments show that our belief revision logic and sequential pattern mining based intelligent information agents outperform the vector space model based information agents. Our work opens the door to the development of next generation of intelligent information agents to alleviate the information overload problem.

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A study on sequential pattern mining on chemical information

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.33.14828 ◽

2018 ◽

Vol 7 (3.3) ◽

pp. 532

Author(s):

S Sathya ◽

N Rajendran

Keyword(s):

Data Mining ◽

Chemical Bonding ◽

Sequential Analysis ◽

Pattern Mining ◽

Fundamental Problem ◽

Research Work ◽

Sequential Pattern Mining ◽

Sequential Pattern ◽

Graph Representation ◽

Chemical Information

Data mining (DM) is used for extracting the useful and non-trivial information from the large amount of data to collect in many and diverse fields. Data mining determines explanation through clustering visualization, association and sequential analysis. Chemical compounds are well-defined structures compressed by a graph representation. Chemical bonding is the association of atoms into molecules, ions, crystals and other stable species which frame the common substances in chemical information. However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications. In this work, chemical structured index bonding is used for sequential pattern mining. Our research work helps to evaluate the structural patterns of chemical bonding in chemical information data sets.

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