Sequence data mining in search of hookworm ( Necator americanus ) microRNAs

Data mining is an interesting focus in computer science field now This paper deals with data mining techniques based on Grey system theories for time sequence data. Firstly, thoughts of data mining with embedded knowledge are expatiated, and the status quo of Data mining techniques is presented briefly. Then, based on the above thoughts and the Grey system theories, data mining techniques based on Grey system theories for time sequence data are proposed for the first time, and the idiographic arithmetic with GM(1,1) as an example is introduced in this paper. Last, it forecasts the total homes in 2002~2005 connecting with Internet in Shang Hai City by the arithmetic.

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Sequence Data Mining

10.1007/978-0-387-69937-0 ◽

2007 ◽

Cited By ~ 4

Keyword(s):

Data Mining ◽

Sequence Data

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Biological Sequence Data Mining

Principles of Data Mining and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/3-540-44794-6_19 ◽

2001 ◽

pp. 228-240 ◽

Cited By ~ 1

Author(s):

Yuh-Jyh Hu

Keyword(s):

Data Mining ◽

Sequence Data ◽

Biological Sequence

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G-language Genome Analysis Environment: a workbench for nucleotide sequence data mining

Bioinformatics ◽

10.1093/bioinformatics/19.2.305 ◽

2003 ◽

Vol 19 (2) ◽

pp. 305-306 ◽

Cited By ~ 58

Author(s):

K. Arakawa ◽

K. Mori ◽

K. Ikeda ◽

T. Matsuzaki ◽

Y. Kobayashi ◽

...

Keyword(s):

Data Mining ◽

Nucleotide Sequence ◽

Genome Analysis ◽

Sequence Data ◽

Nucleotide Sequence Data ◽

Analysis Environment

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Discovering Patterns in Order to Detect Weak Signals and Define New Strategies

Pattern Discovery Using Sequence Data Mining ◽

10.4018/978-1-61350-056-9.ch012 ◽

2012 ◽

pp. 195-211 ◽

Cited By ~ 1

Author(s):

Anass El Haddadi ◽

Bernard Dousset ◽

Ilham Berrada

Keyword(s):

Data Mining ◽

Correspondence Analysis ◽

Sequence Data ◽

Multiple Correspondence Analysis ◽

Decision Makers ◽

Competitive Intelligence ◽

Weak Signals ◽

New Strategies

Competitive intelligence activities rely on collecting and analyzing data in order to discover patterns from data using sequence data mining. The discovered patterns are used to help decision-makers considering innovation and defining the strategy for their business. In this chapter we present four methods for discovering patterns in the competitive intelligence process: “correspondence analysis,” “multiple correspondence analysis,” “evolutionary graph,” and “multi-term method.”

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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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