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While large-scale satellite data could serve to detect landslide bodies, smaller scale data will help in detection of parameters triggering it or characterizing it.
Further empirical research will be undertaken, at least on smaller scale data sets, for example in Germany, in order to compare our results on the European level.
The distance between cell lines and centroids is mostly in a much smaller scale (data not shown) compared with the observed distance.
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Given the aforementioned theoretical work plus our own simulation of a smaller scaled data set and reduction of the high dimensional numeric data (see Additional file 1), we are convinced that the clustering of the samples using the mod k-prototypes algorithm is not dependent on the scale or dimensionality of the data.
The most common models used today rely heavily upon empirical information obtainable only from pilot size experiments and this makes "a priori" design from small scale data difficult.
Exhaustive algorithms, for example, MDR [ 5], appear promising for small scale data sets.
Parameter values tend to be justified based on previous work or trained on a small scale data rather identified as part of a large-scale optimization.
Previous research suggests that sex-selective abortions occur in Nepal, but the evidence is either qualitative or based on small scale data.
Furthermore, these methods are experimented on very small scale data sets (<30 SNPs) of certain complex diseases, performance of which on various kinds of large scale data sets are still unclear.
In practical engineering, small-scale data sets are usually sparse and contaminated by noise.
Very small-scale data are shown from the CRISTA 2 mission.
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