Abstract

Code synthesis is routinely used in industry to generate GUIs, form lling applications, and database support code and is even used with COBOL. In this paper we consider the question of whether code synthesis could also be applied to the data mining phase of knowledge discovery. We view this as a rapid prototyping method. Rapid prototyping of statistical data analysis algorithms would allow experienced analysts to experiment with di erent statistical models before choosing one, but without requiring prohibitively expensive programming e orts. It would also smooth the steep learning curve often faced by novice users of data mining tools and libraries. Finally, it would accelerate dissemination of essential research results and the development of applications. In this paper, we present a framework and the basic software for the automated synthesis of data analysis programs. We use a speci cation language that generalizes Bayesian networks, a popular notation used in many communities. Using decomposition methods and algorithm templates, our system transforms the network through several levels of representation and then nally into pseudocode which can be translated into the implementation language of choice. Here, we explain the framework on a mixture of Gaussians model, a core data mining algorithm at the heart of many commercial clustering tools. We mention the e ectiveness of our framework by generating pseudocode for some more sophisticated algorithms from recent literature.

Citations

612	A view of the EM algorithm that justifies incremental, sparse, and other variants - Neal, Hinton - 1998
477	Distributional clustering of english words - Pereira, Tishby, et al. - 1993
354	Graphical Models in Applied Multivariate Statistics - Whittaker - 2009
213	Operations for learning with graphical models - Buntine - 1994
125	Independence properties of directed Markov fields - Lauritzen, Dawid, et al. - 1990
61	BUGS: A program to perform Bayesian inference using Gibbs sampling - Thomas, Spiegelhalter, et al.
49	Factorial learning and the EM algorithm - Ghahramani - 1994
49	Generating bayesian networks from probability logic knowledge bases - Haddawy - 1994
27	Graphical models for discovering knowledge - Buntine - 1995
26	E¢ cient inference in Bayes nets as a combinatorial optimization problem - Li, D’Ambrosio - 1994
26	A view of the EM algorithm that justi es incremental, sparse, and other variants - Neal, Hinton - 1998
23	Amphion: Automatic programming for scientific subroutine libraries - Lowry, Philpot, et al. - 1994
19	Independence properties of directed Markov elds - Lauritzen, Dawid, et al. - 1990
15	Westfold: Planware – Domain-Specific Synthesis of High-Performance Schedulers - Blaine, Gilham, et al. - 1998
5	Planware: Domain-speci synthesis of high-performance schedulers - Blaine, Gilham, et al. - 1998
4	Automatic synthesis of financial modeling codes - Randall, Kant, et al. - 1996
2	Amphion: Automatic programming for scienti c subroutine libraries - Lowry, Philpot, et al. - 1994
1	Compiler optimizations for high-performance computing - Bacon, Graham, et al. - 1994
1	Analysing rock samples for the mars lander - Oliver, Roush, et al. - 1998
1	Automatic synthesis of nancial modeling codes - Randall, Kant, et al. - 1996