Data mining computational algorithm

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Despite these problems EST databases are a valuable source of large-scale analysis of human variation. They will become even more valuable as the data continue to grow at the present rate. An algorithm for computer-aided SNP mining should contain filters to eliminate the potential sequence errors. Such filters can be based on the probabilistic analysis of sequence features. It can also take into account that multiple occurrences of a variant are more trustworthy, and it may furthermore focus on improving the quality of base-calling if the fluorescent traces are available for closer srcutiny. 

The search or optimization method, the third component, describes the computational procedure to search over parameters and structures. Issues here include the computational methods used to optimize the scoring function and to search related parameters such as the maximum number of iterations or convergence specifications for iterative algorithms. Typical search methods are greedy search, gradient-depen-dent search methods, or breadth search methods [4], One distinguishes between searches that involve only the optimization of the parameters in fixed structures, and the optimization of structures and parameters for data mining methods that include searches over parameter and structure. 

Data mining methods are widely available and can often be highly sophisticated algorithms that use advanced techniques from computer science and artificial intelligence. However, simple and intuitive methods can often work well, without much loss in predictive ability. With small datasets, where the focus is developing interpretable models, these simple methods may be the best first approach, perhaps as part of a conscious elfort to explore the data. In any case it is useful to have some benchmark result against which the performance of more complex, computationally expensive and difficult to interpret methods can be compared. The simplest naive model is prediction by the mean of the dataset, which is in elfect prediction without using a model, and this can serve as a useful reality check and comparator. 

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