5,002 research outputs found
The combination of spatial access methods and computational geometry in geographic database systems
Geographic database systems, known as geographic information systems (GISs) particularly among non-computer scientists, are one of the most important applications of the very active research area named spatial database systems. Consequently following the database approach, a GIS hag to be seamless, i.e. store the complete area of interest (e.g. the whole world) in one database map. For exhibiting acceptable performance a seamless GIS hag to use spatial access methods. Due to the complexity of query and analysis operations on geographic objects, state-of-the-art computational geomeny concepts have to be used in implementing these operations. In this paper, we present GIS operations based on the compuational geomeny technique plane sweep. Specifically, we show how the two ingredients spatial access methods and computational geomeny concepts can be combined für improving the performance of GIS operations. The fruitfulness of this combination is based on the fact that spatial access methods efficiently provide the data at the time when computational geomeny algorithms need it für processing. Additionally, this combination avoids page faults and facilitates the parallelization of the algorithms.
Targeted matrix completion
Matrix completion is a problem that arises in many data-analysis settings
where the input consists of a partially-observed matrix (e.g., recommender
systems, traffic matrix analysis etc.). Classical approaches to matrix
completion assume that the input partially-observed matrix is low rank. The
success of these methods depends on the number of observed entries and the rank
of the matrix; the larger the rank, the more entries need to be observed in
order to accurately complete the matrix. In this paper, we deal with matrices
that are not necessarily low rank themselves, but rather they contain low-rank
submatrices. We propose Targeted, which is a general framework for completing
such matrices. In this framework, we first extract the low-rank submatrices and
then apply a matrix-completion algorithm to these low-rank submatrices as well
as the remainder matrix separately. Although for the completion itself we use
state-of-the-art completion methods, our results demonstrate that Targeted
achieves significantly smaller reconstruction errors than other classical
matrix-completion methods. One of the key technical contributions of the paper
lies in the identification of the low-rank submatrices from the input
partially-observed matrices.Comment: Proceedings of the 2017 SIAM International Conference on Data Mining
(SDM
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Modular feature selection using relative importance factors
Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. In this paper, we explore the use of feature selection in modular GA-based classification. We propose a new feature selection technique, Relative Importance Factor (RIF), to find irrelevant features in the feature space of each module. By removing these features, we aim to improve classification accuracy and reduce the dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that RIF can be used to determine irrelevant features and help achieve higher classification accuracy with the feature space dimension reduced. The complexity of the resulting rule sets is also reduced which means the modular classifiers with irrelevant features removed will be able to classify data with a higher throughput
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