large datasets
Efficient private record linkage of very large datasets
... very large files requires blocking methods to re- duce the number of ...for large datasets. Using Multibit Trees for full census size datasets of CLKs will require additional work, but with ...
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Clustering large datasets using K-means modified inter and intra clustering (KM-I2C) in Hadoop
... years, datasets generated by machines have been large in terms of vol- ume and have been globally distributed ...include datasets that are large and difficult to manage, acquire, store, ...
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PERFORMANCE ANALYSIS OF MAPREDUCE WTH LARGE DATASETS USING HADOOP
... petabytes or terabytes of data can be stored and retrieved easily using these techniques. This paper provides introduction to hadoop HDFS and Mapreduce. In this paper we have used large datasets to analyse ...
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ReCoil - an algorithm for compression of extremely large datasets of dna data
... In this work we design a compression algorithm suitable for very large datasets. As for this task the internal mem- ory is the main bottleneck, ReCoil does not assume that the input or the data structures ...
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Efficient Identification of Approximate Best Configuration of Training in Large Datasets
... five large-scale machine learn- ing benchmarks that are publicly ...handle large datasets and quickly identify the approximate best configura- ...the datasets evaluated in our experiments are ...
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Clustering of large datasets using Hadoop Ecosystem
... analyze large data sets, since the incremental k-means algorithms require to store the cluster membership of each case or to do two nearest-cluster computations as each case is processed, which is computationally ...
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A Review on Density based Clustering Algorithms for Very Large Datasets
... Xin Wang and Howard J. Hamilton presented A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets [1]. They compare two spatial clustering methods. DBSCAN gives ...
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Scaling associative classification for very large datasets
... Attempts to bring the training of an associative classifier onto a framework for paral- lel computing and scale to large datasets have been done in [4, 5]. The authors of [4] proposed a MapReduce solution ...
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EVALUATING COMPETITIVENESS FROM LARGE DATASETS BY MINING COMPETITORS
... We presented a formal definition of competitiveness between two items, which we validated both quantitatively and qualitatively. Our formalization is applicable across domains, overcoming the shortcomings of previous ...
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A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training
... One research area where training set selection has been given attention to is sup- port vector machines (SVM). Generally, these selection methods can be divided into two types, primarily based on whether n is reduced or ...
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A Survey of Clustering Algorithm for Very Large Datasets
... performance by gaining the knowledge on dataset over the course of the execution more interactively and dynamically. The most important contribution of BIRCH is the formation of the clustering problem that is appropriate ...
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A Study on Clustering Algorithms for Large Datasets
... a large and multivariate database A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among ...
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Analysis of Various I/O Methods for Large Datasets in C++
... very large random text files generated by using a PRNG (pseudo random number ...huge datasets, time becomes an important constraint and so using the results of this analysis, a suitable input/output method ...
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Multivariate Statistical Analysis of Large Datasets: Single Particle Electron Microscopy
... the large number of seeds needed to sample the information space sufficiently fine to not miss relevant information hidden in a small corner of factor space, and the low number of classes desired to allow us ...
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Limitations of Co Training for Natural Language Learning from Large Datasets
... 0.96 Accuracy of Left Classifier.. Iterations of Co−Training.[r] ...
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Predicting accuracy on large datasets from smaller pilot data
... We also investigate how details of the regression fit affect the regression accuracy e(n). We exper- ˆ imented with several link functions (we used the default Gaussian link here), but found that these had less impact ...
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Scalable Varied Density Clustering Algorithm for Large Datasets
... uneven datasets of the EDBSCAN, and the idea of multiple representatives taken from the CURE algorithm, and finally the idea of local density taken from the DENCLUE Algorithm) and has overcame their ...
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Detection and Deletion of Outliers from Large Datasets
... ABSTRACT: The paper proposes a method for detecting and deleting distance based outliers in very large data sets. This is based on the outlier detection solving set algorithm. This method introduces parallel ...
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Spatiotemporal Inference and Applications for Large Datasets.
... In this article, we propose an approximate inference method for analyzing massive spatial datasets using Krylov subspace approximation and profile maximum likelihood methods. The method as- sumes that the ...
122
Compressive Review On Mining Competitors From Large Unstructured Datasets
... This method allows us to operationalize our definition of competitiveness and address the problem of finding the top-k competitors of an item in any given market. As we show in our work, this problem presents significant ...
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