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The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering. In this paper, the results obtained by implementing the k-means(More)
This paper describes the results of data mining system developed ad hoc to address the problem of discovering patterns of interest in population databases for cancer. In particular, the experimental results obtained by the system are shown. The architecture of the system is innovative since it integrates a visual cartographic, a data warehouse and a data(More)
Cluster analysis is the study of algorithms and techniques for grouping objects according to their intrinsic characteristics and similarity. A widely studied and popular clustering algorithm is K-Means, which is characterized by its ease of implementation and high computational cost. Although various performance improvements have been proposed for K-Means,(More)
In October 2008, a 15-year-old female alpaca (Vicugna pacos) housed at a breeding farm in northern California died after a brief illness characterized by sudden onset of weakness, recumbency, and respiratory distress. Postmortem examination revealed severe hydrothorax and hydropericardium, marked pulmonary edema, and acute superficial myocardial hemorrhage(More)
We present a method for creating natural language interfaces to databases (NLIDB) that allow for translating natural language queries into SQL. The method is domain independent, i.e., it avoids the tedious process of configuring the NLIDB for a given domain. We automatically generate the domain dictionary for query translation using semantic metadata of the(More)
In this paper we introduce a redesign of the conjugate gradient method for the iterative solution of sparse linear systems on heterogeneous systems accelerated by graphics processing units (GPUs). Reshaping the GPU kernels induced by the classical formulation of the CG method into algorithm-specific routines results in a slight increase of performance and,(More)
This paper deals with heuristic algorithm selection, which can be stated as follows: given a set of solved instances of a NP-hard problem, for a new instance to predict which algorithm solves it better. For this problem, there are two main selection approaches. The first one consists of developing functions to relate performance to problem size. In the(More)