Knowledge discovery in databases (KDD) is the process of discovering interesting knowledge from large amounts of data. However, real-world datasets have problems such as incompleteness, redundancy, inconsistency, noise, etc. All these problems affect the performance of data mining algorithms. Thus, preprocessing techniques are essential in allowing knowledge to be extracted from data. This work presents a real world application of knowledge discovery in databases, with the objective of prediction of bankruptcy. For this task fuzzy classification models based on fuzzy clustering are used, which are developed solely from numerical data. This data set has missing values, extreme values and also presents a much smaller bankruptcy class than the not bankruptcy class, which makes it a challenging problem in the scope of KDD.

Results are presented from a set of experiments designed to investigate factors that may influence proxy-based reconstructions of large-scale temperature patterns in past centuries. The factors investigated include (1) the method used to assimilate proxy data into a climate reconstruction, (2) the proxy data network used, (3) the target season, and (4) the spatial domain of the reconstruction. Estimates of hemispheric-mean temperature are formed through spatial averaging of reconstructed temperature patterns that are based on either the local calibration of proxy and instrumental data or a more elaborate multivariate climate field reconstruction approach. The experiments compare results based on the global multi-proxy data set used by Mann and co-workers, with results obtained using the extratropical Northern Hemisphere (NH) maximum latewood tree-ring density set used by Briffa and co-workers. Mean temperature reconstructions are compared for the full NH (tropics and extratropics, land and ocean), and extratropical continents only, and varying target season (cold-season half year, warm-season half year, and annual mean). The comparisons demonstrate dependence of reconstructions on seasonal, spatial, and methodological considerations, emphasizing the primary

Abstract. The problem of feature selection is crucial for many applications and has thus been studied extensively. However, most of the existing methods are designed to handle data consisting only in categorical or in real-valued features while a mix of both kinds of features is often encountered in practice. This paper proposes an approach based on mutual information and the maximal Relevance minimal Redundancy principle to handle the case of mixed data. It combines aspects of both wrapper and filter methods and is well suited for regression problems. Experiments on artificial and real-world datasets show the interest of the methodology. 1

In matrix completion, we are given a matrix where the values of only some of the entries are present, and we want to reconstruct the missing ones. Much work has focused on the assumption that the data matrix has low rank. We propose a more general assumption based on denoising, so that we expect that the value of a missing entry can be predicted from the values of neighboring points. We propose a nonparametric version of denoising based on local, iterated averaging with meanshift, possibly constrained to preserve local low-rank manifold structure. The few user parameters required (the denoising scale, number of neighbors and local dimensionality) and the number of iterations can be estimated by cross-validating the reconstruction error. Using our algorithms as a postprocessing step on an initial reconstruction (provided by e.g. a low-rank method), we show consistent improvements with synthetic, image and motion-capture data. Completing a matrix from a few given entries is a fundamental problem with many applications in machine learning, computer vision, network engineering, and data mining. Much interest in matrix

Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are “holes ” in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure. Copyright © 2007 Simone Fiori. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.