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Table 1 Main classes of data mining approaches to gene mapping, characterised by three criteria: 1) Descriptive methods primarily aim to recognise the ancestral, shared chromosomal segments identical by descent, whereas predictive methods directly associate with the disease status

From: A survey of data mining methods for linkage disequilibrium mapping

Approach

Methods

 

Characteristics

 

Classification

RP,[5, 6] SDA,[7] DICE,[10] MDR,[11]

SVMs [13], Association rules [9]

Predictive

Haplotype and

subject-oriented

Models interactions

Haplotype clustering

HapMiner,[16] CLADHC,[17]

Spatial clustering [18]

Descriptive

Haplotype and

subject-oriented

No interactions

Phenotype clustering

MCA [19]

Predictive

Subject-oriented

No interactions

Haplotype

patterns

HPM [20] and derivatives,[21, 22, 2628]

TreeDT [30]

Descriptive

Haplotype-oriented

Can model few interactions

  1. 2) Some approaches try to partition the set of subjects into homgeneous groups, some emphasise local similarities in haplotypes, and some are compromises between these extremes. 3) The suitability for describing and computing interactions varies between approaches.