A Descriptive Framework for the Field of Data Mining and Knowledge Discovery

s of forty-nine regular papers from PAKDD 2005 [Ho et al. 2005], which were not used in the framework building process, were collected and analyzed to see if they fit in the categories identified by grounded theory. The abstract of each article was analyzed to identify the primary objective(s) the author(s) are addressing. Take the article “Adjusting Mixture Weights of Gaussian Mixture Model via Regularized Probabilistic Latent Semantic Analysis” by Si and Jin [2005] as an example. The abstract of this article is: Mixture models, such as Gaussian Mixture Model, have been widely used in many applications for modeling data. Gaussian mixture model (GMM) assumes that data points are generated from a set of Gaussian models with the same set of mixture weights. A natural extension of GMM is the probabilistic latent semantic analysis (PLSA) model, which assigns different mixture weights for each data point. Thus, PLSA is more flexible than the GMM method. However, as a tradeoff, PLSA usually suffers from the overfitting problem. In this paper, we propose a regularized probabilistic latent semantic analysis model (RPLSA), which can properly adjust the amount of model