Titlebook: ;

訓(xùn)誡

avarice

Data Preparation,reater detail (see Chaps.?7ff), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Before we start modeling, we have to prepare our data set appropriately, that is, we are going to modify our dataset so that the modeling techniques are best supported but least biased.

motor-unit

geometrician

Florian Mayer,Dennis Schoenebornan overview of clustering methods (hierarchical clustering, k-Means, density-based clustering), association analysis, self-organizing maps and deviation analysis. The definition and choice of distance or similarity measures, which is required by almost every technique to compare different cases in the database, is also tackled.

carbohydrate

合唱隊

芭蕾舞女演員

Innocence

https://doi.org/10.1007/978-94-011-7819-8erstanding phase, we know much better whether the assumptions we made during the project understanding phase concerning representativeness, informativeness, data quality, and the presence or absence of external factors are justified.

游行

ascend