A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Introduction. Matrix Algebra. Characterizing and Displaying Multivariate Data. The Multivariate Normal Distribution. Tests on One or Two Mean Vectors. Multivariate Analysis of Variance. Tests on Covariance Matrices. Discriminant Analysis: Description of Group Separation. Classification Analysis: Allocation of Observations to Groups. Multivariate Regression. Canonical Correlation. Principal Component Analysis. Factor Analysis. Cluster Analysis. Graphical Procedures. Tables. Answers and Hints to Problems. Data Sets and SAS Files. References. Index. <s> BIB001 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> This paper first provides an overview of data preprocessing, focusing on problems of real world data. These are primarily problems that have to be carefully understood and solved before any data analysis process can start. The paper discusses in detail two main reasons for performing data preprocessing: i problems with the data and ii preparation for data analysis. The paper continues with details of data preprocessing techniques achieving each of the above mentioned objectives. A total of 14 techniques are discussed. Two examples of data preprocessing applications from two of the most data rich domains are given at the end. The applications are related to semiconductor manufacturing and aerospace domains where large amounts of data are available, and they are fairly reliable. Future directions and some challenges are discussed at the end. <s> BIB002 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> This paper explores the links between (perceived) environmental risk and community (re) action in an urban industrial neighbourhood in Hamilton, Ontario, Canada. In-depth interviews were conducted with residents of an area with a documented history of adverse air quality, in order to determine the relative influence of social capital (networks, norms, and social trust) and place attachment (sense of belonging in a neighbourhood) in deciding to take civic action around this particular environmental issue. The interviews illustrate the complexity of lay understandings of air pollution, and indicate that social capital is a primary contributor to the decision to take certain kinds of action, while attachment to place plays a lesser role. <s> BIB003 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data quality is a major concern in Machine Learning and other correlated areas such as Knowledge Discovery from Databases (KDD). As most Machine Learning algorithms induce knowledge strictly from data, the quality of the knowledge extracted is largely determined by the quality of the underlying data. One relevant problem in data quality is the presence of missing data. Despite the frequent occurrence of missing data, many Machine Learning algorithms handle missing data in a rather naive way. Missing data treatment should be carefully thought, otherwise bias might be introduced into the knowledge induced. In this work, we analyse the use of the k-nearest neighbour as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set by some plausible values. Our analysis indicates that missing data imputation based on the k-nearest neighbour algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data. <s> BIB004 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Fairly rapid environmental changes call for continuous surveillance and decision making, areas where IT technologies can be valuable. In the aforementioned context this work describes the application of a novel classifier, namely σFLNMAP, for estimating the ozone concentration level in the atmosphere. In a series of experiments on meteorological and air pollutants data, the σ–FLNMAP classifier compares favorably with both back-propagation neural networks and the C4.5 algorithm; moreover σ–FLNMAP induces only a few rules from the data. The σ–FLNMAP classifier can be implemented as either a neural network or a decision tree. We also discuss the far reaching potential of σ–FLNMAP in IT applications due to its applicability on partially (lattice) ordered data. <s> BIB005 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to" information? And are you, like most analysts, preparing the data in SAS? ::: ::: This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection. ::: ::: FEATURES ::: * A complete framework for the data preparation process, including implementation details for each step. ::: * The complete SAS implementation code, which is readily usable by professional analysts and data miners. ::: * A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction. ::: * Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros. ::: * CD includes dozens of SAS macros plus the sample data and the program for the book's case study. <s> BIB006 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. ::: ::: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. <s> BIB007 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Numerous industrial and research databases include missing values. It is not uncommon to encounter databases that have up to a half of the entries missing, making it very difficult to mine them using data analysis methods that can work only with complete data. A common way of dealing with this problem is to impute (fill-in) the missing values. This paper evaluates how the choice of different imputation methods affects the performance of classifiers that are subsequently used with the imputed data. The experiments here focus on discrete data. This paper studies the effect of missing data imputation using five single imputation methods (a mean method, a Hot deck method, a Nai@?ve-Bayes method, and the latter two methods with a recently proposed imputation framework) and one multiple imputation method (a polytomous regression based method) on classification accuracy for six popular classifiers (RIPPER, C4.5, K-nearest-neighbor, support vector machine with polynomial and RBF kernels, and Nai@?ve-Bayes) on 15 datasets. This experimental study shows that imputation with the tested methods on average improves classification accuracy when compared to classification without imputation. Although the results show that there is no universally best imputation method, Nai@?ve-Bayes imputation is shown to give the best results for the RIPPER classifier for datasets with high amount (i.e., 40% and 50%) of missing data, polytomous regression imputation is shown to be the best for support vector machine classifier with polynomial kernel, and the application of the imputation framework is shown to be superior for the support vector machine with RBF kernel and K-nearest-neighbor. The analysis of the quality of the imputation with respect to varying amounts of missing data (i.e., between 5% and 50%) shows that all imputation methods, except for the mean imputation, improve classification error for data with more than 10% of missing data. Finally, some classifiers such as C4.5 and Nai@?ve-Bayes were found to be missing data resistant, i.e., they can produce accurate classification in the presence of missing data, while other classifiers such as K-nearest-neighbor, SVMs and RIPPER benefit from the imputation. <s> BIB008 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Summary. Compositional data record the relative proportions of different components within a mixture and arise frequently in many fields. Standard statistical techniques for the analysis of such data assume the absence of proportions which are genuinely zero. However, real data can contain a substantial number of zero values. We present a latent Gaussian model for the analysis of compositional data which contain zero values, which is based on assuming that the data arise from a (deterministic) Euclidean projection of a multivariate Gaussian random variable onto the unit simplex. We propose an iterative algorithm to simulate values from this model and apply the model to data on the proportions of fat, protein and carbohydrate in different groups of food products. Finally, evaluation of the likelihood involves the calculation of difficult integrals if the number of components is more than 3, so we present a hybrid Gibbs rejection sampling scheme that can be used to draw inferences about the parameters of the model when the number of components is arbitrarily large. <s> BIB009 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed. <s> BIB010 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided. <s> BIB011 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on a classification task with twenty-three classification methods and fourteen different imputation approaches to missing values treatment that are presented and analyzed. The analysis involves a group-based approach, in which we distinguish between three different categories of classification methods. Each category behaves differently, and the evidence obtained shows that the use of determined missing values imputation methods could improve the accuracy obtained for these methods. In this study, the convenience of using imputation methods for preprocessing data sets with missing values is stated. The analysis suggests that the use of particular imputation methods conditioned to the groups is required. <s> BIB012 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Fifty years after the hyporheic zone was first defined (Orghidan, 1959), there are still gaps in the knowledge regarding the role of biodiversity in hyporheic processes. First, some methodological questions remained unanswered regarding the interactions between biodiversity and physical processes, both for the study of habitat characteristics and interactions at different scales. Furthermore, many questions remain to be addressed to help inform our understanding of invertebrate community dynamics, especially regarding the trophic niches of organisms, the functional groups present within sediment, and their temporal changes. Understanding microbial community dynamics would require investigations about their relationship with the physical characteristics of the sediment, their diversity, their relationship with metabolic pathways, their inter- actions with invertebrates, and their response to environmental stress. Another fundamental research question is that of the importance of the hyporheic zone in the global metabolism of the river, which must be explored in relation to organic matter recycling, the effects of disturbances, and the degradation of contaminants. Finally, the application of this knowledge requires the development of methods for the estimation of hydro- logical exchanges, especially for the management of sediment clogging, the optimization of self-purification, and the integration of climate change in environmental policies. The development of descriptors of hyporheic <s> BIB013 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> The importance of post-processing the results of clustering when using data mining to ::: support subsequent decision-making is discussed. Both the formal embedded binary logistic ::: regression (EBLR) and the visual profile’s assessment grid (PAG) methods are presented ::: as bridging tools for the real use of clustering results. EBLR is a sequence of logistic ::: regressions that helps to predict the class of a new object; while PAG is a graphical tool that ::: visualises the results of an EBLR. PAG interactively determines the most suitable class for a ::: new object and enables subsequent follow-ups. PAG makes the underlying mathematical ::: model (EBLR) more understandable, improves usability and contributes to bridging the gap ::: between modelling and decision-support. When applied to medical problems, these tools ::: can perform as diagnostic-support tools, provided that the predefined set of profiles refer ::: to different stages of a certain disease or different types of patients with a same medical ::: problem, etc. Being a graphical tool, PAG enables doctors to quickly and friendly determine ::: the profile of a patient in the everyday activity, without necessarily understanding the ::: statistical models involved in the process, which used to be a serious limitation for wider ::: application of these methods in clinical praxis. In this work, an application is presented ::: with 4 functional disability profiles. <s> BIB014 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> K. Gibert et al. / A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> In real applications, important rates of missing data are often found and have to be preprocessed before the analysis. The literature for missing imputation is abundant. However, the most precise imputation methods require long time, and sometimes specific software; this implies a significant delay to get final results. The Mixed Intelligent-Multivariate Missing Imputation (MIMMI) method is proposed as a hybrid missing imputation methodology based on clustering. The MIMMI is a non-parametric method that combines the prior expert knowledge with multivariate analysis without requiring assumptions on the probabilistic models of the variables (normality, exponentiality, etc.). The proposed imputation values implicitly take into account the joint distribution of all variables and can be determined in a relatively short time. The MIMMI uses the conditional mean according to the self-underlying structure of the data set. It provides a good trade-off between accuracy and both simplicity and required time to data preparation. The mechanics of the method is illustrated with some case-studies, both synthetic and real applications related with human behaviour. In both cases, acceptable quality results were obtained in short time. <s> BIB015
nificant added value by analyzing publications either in social networks or web pages BIB007 BIB003 . Some public resources are available to process this kind of textual information and obtain new variables, like the intensity of the earthquake  . Thus, building the original data matrix means to make data fusion  BIB002 with all information coming from the different sources, and eventually merge several independent data matrices into a single one containing all available information for each data row BIB006 . Finally, one of the current problems in environmental data representation is the lack of a standard environmental data format, despite some proposals and efforts of standardization made. Environmental data sharing is classically based on sharing data format, measurement methods, analysis methods and instrumentations. On the other hand, the standardization of the environmental data aims at sharing the data structure in order to share information among various bodies. Since environmental data possess coordinates on the globe, it can be treated as a form of geographical information. Then data structuring definition work is done by the ISO/TC211 [116,  . If this is applied, then environmental data may be standardized in the same way as other geographical information. The ISO/TC211 considers that geographical information comes from geographical feature data and meta-data  . One of the most promising directions has been the use of XML files to encode environmental data. XML is becoming an increasingly common format for data representation in data mining domains due to its expressiveness, flexibility, and cross-platform nature. An example is Extended Environments Markup Language (EEML), which is a protocol for sharing sensor data between remote responsive environments, both physical and virtual, initially aimed for construction purposes  . As an example, in the analysis of river's retention BIB013 , one could get different databases. One containing morphologic characteristics of the river, built by experts; another containing biochemical characteristics of the rivers at different timestamps, provided by a biochemical laboratory; another containing information on microbiological diversity, provided by a set of biologists, and based on microscope observation of samples; another with some satellite pictures to learn land uses around the river; and another with some sensor data about flows along time. The very first step of preprocessing is to properly transform all this information into a single data matrix where rivers are in the rows and all flow, land use, morphologic, biochemical and microbiological, characteristics are expressed as columns in the data matrix with values at the different timestamps (one row per timestamp). This single data matrix is the one used as original data matrix to pass to the next step of pre-processing. • Which data matrix rows (instances) are going to be kept for the analysis? • Which data matrix columns (variables) are going to be considered in the analysis? in automatic repetitive KDD procedures, since they do not require human interaction to decide: the χ 2 -tests for independence, or the Shapiro-Wilks for normality  , the assessment of multivariate normality it is not so easy, but Mardia test can be used BIB001 . However, these tests usually have their own technical assumptions in turn and it is important to ensure these last hold to guarantee the reliability of the significance test results Thus, by making previous visualization of data, one can easily point out which kind of pre-processing operations are required either related to intrinsic data quality, or to make available the application of some particular Data Mining method. and will follow the general model ( Fig. 4 ). Then, it is convenient to keep it for the analysis. When the outlier is a numerical code used for representing missing data (i.e. 999999), it is convenient to substitute it by the missing code and proceed with missing data treatment (imputation). It is of utmost importance to report any decision made over outliers in a clear and explicit way that permits the end user to properly evaluate if something is wrong with the data or outlier treatment decisions must be revisited. Sometimes, missing cells are marked as a *, ?, NA (Not Available), blank space or other special character or special numeric code such as 99999. Blank space can be problematic when imputing the data matrix in some specific software, as it might happen that the software does not detect the blank and shifts values of the row one position to the right. Numerical codes assigned to missing data like 99999 can produce severe mistakes in calculations if not properly treated, as they can be wrongly incorporated in the analysis. Missing code automatically recognized by the software ("*" in Minitab, "?" in Weka, "NA" in R package, etc.) can also be problematic, if the user is not aware of the implicit assumptions that the software is making over data, and missing treatment. Distance-based methods can be used. The most basic approach in this line is using the k-NN BIB004 . The idea is to use distances to identify the closest instance in the data base to the one containing the missing, and to use the value of the nearest neighbor for imputation. This can be extended to consider the mean value of the first k nearest neighbors as imputation value. The method is quick and performs well, provided that the variables used for the k-NN are complete, i.e. do not contain missing values. Otherwise, distances that can deal with missing values in turn are required, and standard softwares do not implement them. Other distancebased works are  . The MICE method BIB011 solves in an iterative process the existence of missings in the other variables. Each variable is imputed based on a regression model built with the remaining variables. All missing data of regressors need to be previously imputed. They are initially imputed by the mean, and all variables imputed iteratively until convergence. The MIMMI method BIB015 is based on performing a previous clustering with a subset of complete variables and to impute missing values of the remaining variables with the conditional means of discovered clusters. Such an approach may even help to identify errors in the dataset (when some observation is placed in a wrong cluster and the reasons are investigated). Another method proposed in BIB005 was to use qualitative indicators (i.e. by assigning quality labels instead of predicting values) for substituting missing or erroneous values. Similar work has been done for substituting missing or erroneous values with fuzzy sets, probability distributions or confidence intervals. Sometimes, the best imputation method depends on the particular data mining method to be applied afterwards BIB008 BIB012 . It is important to impute missing data before using the corresponding variable to create other variables or indicators, in order not to propagate the missings to the new variables. It is of outmost importance to report the missing imputation method used explicitly for allowing the end user to evaluate if the missing imputation decisions are acceptable or not for each specific application. provided by experts wants to be technically refined, some automatic methods should be used. Feature weighting methods quantify the relevance of the variables. Feature selection identifies the subset of relevant variables. Factorial methods build a smaller set of new synthetic variables conserving most information from the working data matrix. When the number of variables is too high to deal with in a reasonable way, which is not unusual in data mining context, a data reduction method can be applied. Either Factorial methods or Feature Weighting or Feature Selection are suitable possibilities in these cases. Next subsections give details. build X k = log(X k /X j ), for (k = 1 : s) and use X k in modeling. In BIB009 a nice introduction to the compositional data treatment is given and alternative transformations which make sense in some specific situations are discussed. Note that sometimes, the compositional data are hidden in the dataset. This means that the components appear in non-contiguous columns; sometimes they are presented as concentrations or absolute numbers and they are not directly expressed as proportions. For example, in a microscopic observation of a water sample, it is usual to account for the number of specimens of different families of microorganisms. Thus, the dataset contains some columns with counts of individuals for different families, which need to be transformed into proportions with respect to the total number of specimens counted and treated as compositional data. getting to check some data issues gives problems during the data mining step and requires costly backtracking to the pre-processing step thus forcing to rebuild the whole analysis several times. In this paper, the most relevant steps involved in pre-processing with environmental data are identified and reviewed, and a general framework and methodology are proposed for dealing with the pre-processing from an applied perspective in the Data Mining process, considering that pre-processing is highly related with data quality issues and it is of paramount importance to avoid propagation of errors within the data mining models and consequent decision-making based on them. As it can be seen, the pre-processing step is strongly multidisciplinary, and requires integrated tools from many different origins, including basic Statistics, multivariate analysis, machine learning, information management, visualization and GIS, among others. The most relevant aspects of each issue in preprocessing have been reviewed and structured in a way that the reader can effectively use them to make decisions on how to pre-process a real dataset. As a result of the state-of-the-art review performed in this paper, and the efforts in conceptualization of the different sections, like the one about outliers or missing data, some guidelines for pre-processing tasks are synthetized here, as a first approach to provide a global guide to perform correct KDD processes as a whole. The main contribution of this paper is to provide a deep analysis of the steps composing the preprocessing task. It can be said that, in general, most of the pre-processing techniques are devoted to: visualization of the data, detection of imperfections or verification of the accomplishment of some properties of the dataset, transformation of data for correcting imperfections or achieving certain properties for better analysis. This include a number of different operations that make sense in different scenarios suitable in environmental data mining, which often are related with the nature of data itself, but also with the goals of the analysis and the related domain knowledge. Finally, as a last contribution of the paper, the enormous effort in reviewing the work done in the literature in pre-processing, allowed to identify the hot issues and challenging aspects in and of the interdisciplinary field of environmental DM in coming years. The achievement of some specific goals, non-well established yet, at the level of both end-users and developers or researchers would increase the benefits of a proper pre-processing process and improve the quality of the further data mining modeling step. At the level of end-users the following issues are important: • Get acquainted with available methods and technical assumptions of pre-processing methods, even before data collection, if possible. • Try to clarify the relevant questions to be answered by the KDD process to orient an adequate Data Mining. Do not lose perspective of final usefulness. • Consider all data available which might be relevant for the KDD goal, independently of their original format (images, text, etc.) and use data fusion to get the initial data matrix. Try to minimize the missing data collected by properly designing the structural missing data management. Consider the collection of qualitative variables without internal tedious numerical encodings when possible. • Collaboration between the expert and the data miner becomes crucial in most of the decisions made during the pre-processing step. • Be careful and rigorous on meta-data collection. It is important to store it in an available format and to clearly understand it for proper analysis decisions, and proper data and results interpretation. • Reserve a relevant time in your projects for data pre-processing and devote the required time to it, by performing it carefully enough to guarantee the quality of the data mining models results. • Carefully verify that missing data is properly imputed in the pre-processing tool, and transfer to the system as much meta-information as possible. • Select relevant data for the working data matrix before starting the pre-processing analysis itself. To this purpose, KDD goals, and priori specific domain knowledge has to be taken into account. In case of doubt, it is preferable to keep a variable and make decisions at modeling step. • Use visualization to decide which pre-processing aspects need to be treated. • Try to change your data as little as possible; make the minimum pre-processing operations required to improve data quality and guarantee better interpretability of the final data mining results. Try to avoid, as much as possible, transformations that generate variables without a meaning that can make data mining models understandable. Instead of adapting data to the data mining method technical requirements, try to find a method that fits well with the intrinsic structure of your dataset. • However, spend all required time in terminology standardization. • Pay special attention to imbalanced datasets, compositional data, and multivalued variables. Consider the available possibilities to deal with data noise and uncertainty. • Whenever possible, include in the dataset synthetic variables approaching the decisional indicators used by experts to reason. • Use both instance selection and/or feature selection to detect redundant or irrelevant variables or instances, reducing the dimension of the dataset by keeping the quality of the data mined models. • When appropriate, use feature weighting to estimate the relevance of the variables of the data matrix, and make further decisions accordingly. • Try resampling to check stability of results and to process too big datasets with a particular limited method. Use the classical statistical sampling principles, traditionally used to get samples from real populations, to sample over a big dataset. • Ensure that conclusions are well framed into the subpopulations determined by the pre-processing step really analyzed. • Always report in a transparent way all pre-processing assumptions done, and all pre-processing decisions made, particularly those related to elimination of some rows or variables, or modifications owing to error correction. At the level of researchers, the following issues merit serious attention in the near future, in order to advance the state of the art in pre-processing in the challenging directions that will make it easier, more powerful and reliable in real applications: • The development of meta-data management support tools that permit to standardize the metadata representation models, and the way they can be used in the pre-processing is of main interest, since there is a lack of standards and all the responsibility relies nowadays to the end user, this increasing the risks of disregarding important details. Also, to develop mechanisms to introduce these standards into the data mining system is relevant, since currently only basic issues like declaring qualitative variables is available in a reduced number of data mining software tools. This will permit automation of some decisions, increasing efficiency of the pre-processing step. • Elaborate protocols to enhance data sharing and reuse, particularly oriented to build data matrices to be mined, by combining different data sources from different formats. In particular, the potential of web/text mining needs to be further explored. • There is a great need of developing methods for data fusion able to deal simultaneously with data coming from different sources, with different natures, scales, granularities and formats. Particular attention on getting data from smart sensors, images or web documents is suggested. • Particular attention is required to explore the challenges for properly pre-processing incremental data and also the specific considerations required for pre-processing in big data. • More research is still required to better understand the complexity of pre-processing tasks in the context of KDD processes. The methodological proposal presented in this paper is a first attempt to systematize the pre-processing process, which can be used to guide the flow of pre-processing operation in data mining software tools. However, additional research is required to stabilize a complete and exhaustive methodology for pre-processing in environmental systems. • As it has been extensively illustrated along the paper, it is very relevant to take into account the expertise of the end-user as well as the specific domain knowledge to guide many decisions and operations required in the pre-processing step. For this reason, a methodology that considers the involvement of both prior domain knowledge and interaction with the expert is essential for the progress in the pre-processing field. • As many Data Mining softwares provide GUIs to design the workflow for a complete KDD process, collect KDD experiences in a workflow library and develop Data Mining strategies to mine the workflows themselves for improving the preprocessing, data mining and post-processing recommenders under an evidence-based approach are much needed. • Development of standard procedures (benchmarks) for experimental testing and validation of new pre-processing tools. • There is a lack of tools for explicit representation and handling of discovered knowledge for greater understandability. Development of tools to bridge the gap between modeling and effective decisionmaking   BIB010 BIB014 can be enormously useful at all levels of data mining models, to guarantee the effective impact of the data mining step at an environmental decision-making level. As in ESs it is usual to meet decision-makers with no technical skills, development of this kind of tools is more needed than in other more technical fields. • Finally, there is a need of moving towards the development of integrated KDD-systems, with sufficient intelligence to pursue integral approaches, covering the whole KDD process, from problem formulation to discovered knowledge production, including interpretation of results. This requires KDD systems including data fusion, meta-data management, data cleaning and the whole range of pre-processing techniques, taking into account prior expert knowledge, data mining method recommendation, and post-processing, and involving the expert in both the problem formulation, preferences on kind of results and interpretation. Regarding the support provided to pre-processing by the various available KDD software packages, we can say that, although they often include some available tools to perform some pre-processing steps, current packages seem still to be far from being such a complete intelligent system that could support both a well systematized pre-processing and an integral KDD, as stated above. The complete pre-processing process is normally designed from scratch for every application and only lists of unconnected techniques for preprocessing are provided in most softwares. We strongly believe that efforts should be made in the near future in this direction to provide a general framework for preprocessing the data in a reliable way, and any advance on the state of the art in this direction will have benefits not only in the quality of results in data mining projects, but also in the time required for them.