Pattern discovery

Pattern discovery

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Pattern discovery.

 

Introductory reading

Fogel (2008) Computational intelligence approaches for pattern discovery in biological systems. Brief Bioinformatics 9:307-16. (pmid: 18460474)

[ PubMed ] [ DOI ] Abstract Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

 

Contents

Pattern Discovery - Presentation by Omar Waghi, BCB410 - 2011

 

Exercises

Exercises - by Omar Waghi, BCB410 - 2011

 

Further reading and resources

Rajapakse (2009) Computational techniques and pattern recognition: pattern discovery in bioinformatics. IEEE Eng Med Biol Mag 28:16-8. (pmid: 19622419)

[ PubMed ] [ DOI ]

Junjie Wu et al. (2012) Adapting the Right Measures for Pattern Discovery: A Unified View. IEEE Trans Syst Man Cybern B Cybern 42:1203-14. (pmid: 22411027)

[ PubMed ] [ DOI ] Abstract This paper presents a unified view of interestingness measures for interesting pattern discovery. Specifically, we first provide three necessary conditions for interestingness measures being used for association pattern discovery. Then, we reveal one desirable property for interestingness measures: the support-ascending conditional antimonotone property (SA-CAMP). Along this line, we prove that the measures possessing SA-CAMP are suitable for pattern discovery if the itemset-traversal structure is defined by a support-ascending set enumeration tree. In addition, we provide a thorough study on the family of the generalized mean (GM) measure and show their appealing properties, which are exploited for developing the GMiner algorithm for finding interesting association patterns. Finally, experimental results show that GMiner can efficiently identify interesting patterns based on SA-CAMP of the GM measure, even at an extremely low level of support.