data reductionscaling‐upalgorithmsmodel validationChristian H. WeiUniversity of Würzburg Institute of Mathematics Department of Statistics Würzburg GermanyAmerican Cancer SocietyWeiß, C.H.: Sampling in data mining. In: Ruggeri, F., et al. (eds.) Ency- clopedia of Statistics in Quality and ...
If large numbers of people need to be surveyed but cannot be surveyed due to time and resource constraints, a subset of the population of interest, called a sample, must be selected using some type of sampling method. As with data sampling, the sampling method is the technique used to sele...
The fundamental idea of sampling is to estimate the parameters of an entire population or to make inferences about a population based on statistical models and data derived from only a sample of that population. Thus, the intent of sampling is not to describe the particular units that, by ...
None of these samplers work and operate in the exact same way, and it is extremely important for any material processing application to work with an experienced manufacturer or consultant to understand everything from the system layout and integration to the long-term site goals. In general, me...
In the era of big data, the explosive data volume brings new challenges to frequent pattern mining: (1) Space complexity: both input data, intermediate results and the outputted patterns could be too large to fit into memory which prevents many algorithms from executing; (2) Time complexity:...
Text data often exhibits class distribution imbalance, causing classifiers to favor the majority class with a larger number of samples, resulting in freque
The main rea- son for using stratified sampling instead of simple random sampling is improved efficiency of sampling [2,3]. Sampling efficiency is the amount of information obtained for a given sampling cost, and the efficiency of stratified sampling is usually better than the efficiency o...
Data mining is a complex process that aims to derive an accurate pre-dictive model starting from a collection of data. Traditional approaches assume that data are given in advance and their quality, size and structure are independent parameters. In this paper we argue that an extended vision of...
With the rapid expansion of data, the problem of data imbalance has become increasingly prominent in the fields of medical treatment, finance, network, etc. And it is typically solved using the oversampling method. However, most existing oversampling met
Lean Sampling/Data Use a Classification and Regression Tree (CART) for Quick Data Insights Updated: February 27, 2024 by Amit Kumar Ojha In the Analyze phase of a DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma project, potential root causes of variations and defects are ...