
For instance waterway tonnage, as tracked by the U.S. Army Corps of Engineers Navigation Data Center, indicates the monthly tonnage volume of goods traveling across the ocean and other major bodies of water, including the Great Lakes region. Sources such as the Association of American Railroads (AAR) Weekly Rail Traffic Summary provide statistics on the percent change in monthly rail carloads and intermodal units.

Nigeria’s oil resources, on which the country depends for more than 70 per cent of government revenues and more than 90 per cent of hard currency earnings despite the rapid growth of other sectors of the economy

If your variables are of incomparable units (e.g. height in cm and weight in kg) then you should standardize variables, of course. Even if variables are of the same units but show quite different variances it is still a good idea to standardize before Kmeans. You see, Kmeans clustering is "isotropic" in all directions of space and therefore tends to produce more or less round (rather than elongated) clusters. In this situation leaving variances unequal is equivalent to putting more weight on variables with smaller variance, so clusters will tend to be separated along variables with greater variance.
A different thing also worth to remind is that Kmeans clustering results are sensitive to the order of objects in the data set. A justified practice would be to run the analysis several times, randomizing objects order; then average the cluster centres of those runs and input the centres as initial ones for one final run of the analysis.

More formally, do a depth first traversal of the tree and evaluate inherited attributes on the way down and synthetic attributes on the way up. This corresponds to a an Eulertour traversal

Neural networks of this type can have as inputs any real numbers, and they have a real number as output. For regression, it is typical for the output units to be a linear function of their inputs. For classification it is typical for the output to be a sigmoid function of its inputs (because there is no point in predicting a value outside of [0,1]). For the hidden layers, there is no point in having their output be a linear function of their inputs because a linear function of a linear function is a linear function; adding the extra layers gives no added functionality. The output of each hidden unit is thus a squashed linear function of its inputs.
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