Extending Metalearning to Data Mining and KDD

Although a valid intellectual challenge in its own right, metalearning finds its real raison d'être in the practical support it offers Data Mining practitioners. The metaknowledge induced by metalearning provides the means to inform decisions about the precise conditions under which a given algorithm, or sequence of algorithms, is better than others for a given task. Without such knowledge, intelligent but uninformed practitioners faced with a new Data Mining task are limited to selecting the most suitable algorithm(s) by trial and error. With the large number of possible alternatives, an exhaustive search through the space of algorithms is impractical; and simply choosing the algorithm that somehow “appears” most promising is likely to yield subopti-mal solutions. Furthermore, the increased amount and detail of data available within organizations is leading to a demand for a much larger number of models, up to hundreds or even thousands, a situation leading to what has been referred to as Extreme Data Mining [96]. Current approaches to Data Mining remain largely dependent on human efforts and are thus not suitable for this kind of extreme setting because of the large amount of human resources required. Since metalearning can help reduce the need for human intervention, it may be expected to play a major role in these large-scale Data Mining applications. In this chapter, we describe some of the most significant attempts at integrating metaknowledge in Data Mining decision support systems.

While Data Mining software packages (e.g., Enterprise Miner,¹ Clemen-tine,² Insightful Miner,³ PolyAnalyst,⁴ KnowledgeStudio,⁵ We ka, ⁶ Rapid-Miner,⁷ Xelopes⁸) provide user-friendly access to rich collections of algorithms, they generally offer no real decision support to nonexpert end users. Similarly, tools with emphasis on advanced visualization (e.g., [121, 122]) help users understand the data (e.g., to select adequate transformations) and the models (e.g., to adjust parameters, compare results, and focus on specific parts of the model), but treat algorithm selection as an activity driven by the users rather than the system. The discussion in this chapter purposely leaves out such software packaging and visualization tools. The focus is strictly on systems that guide users by producing explicit advice automatically.