Benchmarking the effectiveness of sequential pattern 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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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.

Download Full-text