## 2 Literature Review  
Data mining techniques are being aggressively adopted by the hard sciences mostly in biology and increasingly in chemistry research [2, 3]. Early research focused on quantitative structure-activity relationships (QSARs) models that correlate molecular structure to compound activity [4]. One of the earlier efforts used a survey of expert knowledge to build a \"screening-level\" model for aerobic biodegradability [5]. Original studies that applied Artificial Intelligence for such expert judgment in the modeling of biodegradability used the Inductive Logic Programming to predict the half-life for aerobic aqueous biodegradation [6, 7]. Evaluation and application of models for the prediction of ready biodegradability are reported in the MITI-I test where two methods in which the probability of rapid biodegradation is estimated by Multivariate Linear Regression (MLR) models of biodegradability and counts of fragments of structure and molecular weight. [6]. Another Artificial Intelligence type of modeling biodegradation is an example-based learning system, instead of expert systems [7]. A third effort [8] also applied other Artificial Intelligence models for biodegradability prediction to generate predictive rules using the inductive machine learning approach with structural features as variables, and discretized biodegradability comprising two classes (slow and fast biodegradation). The IUPAC study presented a review and a critical analysis of modeling and estimating the degradability of chemicals in the environment [9]. More recently, various QSARs-based classification techniques to classify different types of base oils were tested [2]. However, available QSAR models have so far proved to be of limited effectiveness since their achieved predictive accuracy varied widely, from 40% to 90% [8].  
An alternative to QSAR modeling used chemical and physical characteristics with analytical modeling of biodegradability as a process assumed to be driven by such attributes. Two studies of this type are of particular interest to our research. The first used Artificial Neural Network models to predict biodegradability of base oils using some of their chemical composition and their viscosity [10]. The second used different characteristics of a variety of oils to investigate the impact of chemical and physical characteristics on the biodegradability of 63 base oils using MLR models [1]. The focus of the former research was to uncover the most significant factors having the highest impact on biodegradability through a global predictive (MLR) model. Their research showed that the main characteristics that significantly affect biodegradability are Paraffinic Carbon (PC) content, KinematicViscosity (as Log), and Noak Volatility (as Log). The resulting MLR models showed that the average error is 30%, and can be as high as 88%.  
Thus, based on the results of modeling efforts to-date, it can be concluded that the biodegradability problem has not been satisfactorily solved. Due to the highly adverse effects of base oils on the environment and human well-being, higher prediction accuracy is of critical significance. The present research investigates the performance of various data mining techniques in predicting and possibly improving the prediction of base oils biodegradability, using the data and the relevant variables reported [1]. The focus here is not on finding general theoretical explanatory models of biodegradability, but it is rather directed toward the identification of modeling techniques that would improve the accuracy of biodegradability prediction.