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04_VDB_submit-02_chapter
04_VDB_submit-02_chapter

... Horizontal data structure has been prove n to be inefficient for data mining on very large sets due to the large cost of scanning. It is of importance to develop vertical data structures and algorithms to solve the scalability issue. Various structures have been proposed, among which P -tree is a ve ...
download
download

... • The relationship between GIS and databases varies. (Heywood, p.81, 2002). • For a simple raster GIS, where one cell in a layer of data contains a single value that represents the attributes of that cell, a database is not necessary. • Here the attribute values are likely to be held in the same fil ...
(Student#).
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Sparse Gaussian Graphical Models with Unknown Block Structure

... penalty can be interpreted as preferring graphs that are sparse, that is, which have few edges. However, this approach is different from standard model selection methods for GGMs, such as (Drton & Perlman, 2004), which estimate the graph structure but not the parameters. For some kinds of data, it i ...
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... Where does Acxiom’s marketing data come from? The data in Acxiom’s marketing products comes from three different types of sources: 1) government records, public records and publicly available data, 2) self-reported data, and 3) data from other commercial entities where consumers have been provided n ...
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... information. Sharing of corporate performance figures is beneficial, but sharing of rumors can be demoralizing. Separating information from non-information is an information management issue. Getting value out of information requires more than a technology. ...
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... It has a set of panels, each of which can be used to perform a certain task. The Preprocess panel, selected in Figure 1, retrieves data from a file, SQL database or URL. (A limitation is that all the data are kept in main memory, so subsampling may be needed for very large datasets.) Then the data c ...
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... table varname1, c(mean varname2) reports the mean of varname2, broken out by categorical varname1. For example, suppose you are working with the yogurtall data. If you type table store, c(mean price1), you will get the mean value of price1 by store. Other statistics such as median, max, min, and sd ...
< 1 ... 25 26 27 28 29 30 31 32 33 ... 76 >

Forecasting

Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms ""forecast"" and ""forecasting"" are sometimes reserved for estimates of values at certain specific future times, while the term ""prediction"" is used for more general estimates, such as the number of times floods will occur over a long period.Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.
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