We present empirical results on computing optimal dominating sets in networks by means of data reduction through preprocessing rules. Thus, we demonstrate the usefulness of so far only theoretically considered reduction techniques for practically solving one of the most important network problems in combinatorial optimization. keywords Graph Theory, Location, Topological Design, Network Models,...

An empirical method for splitting German compounds is explored by varying it in a number of ways to investigate the consequences for factored statistical machine translation between English and German in both directions. Compound splitting is incorporated into translation in a preprocessing step, performed on training data and on German translation input. For translation into German, compounds ...

We revisit the problems of computing the maximal and the minimal non-empty suffixes of a substring of a longer text of length n, introduced by Babenko, Kolesnichenko and Starikovskaya [CPM’13]. For the minimal suffix problem we show that for any 1 ≤ τ ≤ logn there exists a linear-space data structure with O(τ) query time and O(n logn/τ) preprocessing time. As a sample application, we show that ...

We present a classification-based approach to selecting preprocessors of CNF formulas for the complete solver MiniSAT. Three different preprocessors are considered prior to running MiniSAT. To obtain training data for classification, each preprocessor is run, followed by running MiniSAT on its resulting CNF, on instances from the last competition. On each instance, the preprocessor leading to t...

Melanoma is a very dangerous skin cancer. In this paper we present results of experiments on three melanoma data sets. Two data mining tools were used, a new system called IRIM (Interesting Rule Induction Module) and well established LEM2 (Learning from Examples Module, version 2), both are components of the same data mining system LERS (Learning from Examples based on Rough Sets). Typically IR...

We present an algorithm for visibility preprocessing of urban environments. The algorithm uses a subdivision of line space to analytically calculate a conservative potentially visible set for a given region in the scene. We present a detailed evaluation of our method including a comparison to another recently published visibility preprocessing algorithm. To the best of our knowledge the propose...

Rough sets are efficient for data pre-processing in data mining. Matroids are based on linear algebra and graph theory, and have a variety of applications in many fields. Both rough sets and matroids are closely related to lattices. For a serial and transitive relation on a universe, the collection of all the regular sets of the generalized rough set is a lattice. In this paper, we use the latt...

We consider the problems of computing the maximal and the minimal non-empty suffixes of substrings of a longer text of length n. For the minimal suffix problem we show that for every τ , 1 ≤ τ ≤ log n, there exists a linear-space data structure with O(τ) query time and O(n log n/τ) preprocessing time. As a sample application, we show that this data structure can be used to compute the Lyndon de...

The problem of selecting the patterns to be learned by any model is usually not considered by the time of designing the concrete model but as a preprocessing step. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Recently the computation of the mutual information for regression tasks has been proposed...

This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant f...