Classification of data

Classifications of data have two purposes (Hartigan, 1983 Gordon, 1981) data simplification (also called a descriptive function) and prediction. Simplification is necessary because there is a limit to both the volume and complexity of data that the human mind can comprehend and deal with conceptually. Classification allows us to attach a label (or name) to each group of data, to summarize the data (that is, assign individual elements of data to groups and to characterize the population of the group), and to define the relationships between groups (that is, develop a taxonomy). 

Table 9 shows the construction of the ANOVA table. If the variance estimate of a class variable MS variabie deviates significantly from that obtained by that for random error MSettot, then the null hypothesis that the means at the different levels for that variable are equal is rejected. In other words, the classification of data by that variable is explanatory of the variation observed in the data. We conduct the test by using the variance ratio test F = MSvariabie/AfSError, with... 

Continuous Collection and Classification of Data as an Aid in Preparing Surveys... 

LANE AND METSCHL—CONTINUOUS COLLECTION AND CLASSIFICATION OF DATA... 

Concentrations of exhaust components were digitized and recorded by computer every 0.5 seconds during each driving test. In addition, other data such as vehicle speed, throttle position, and fuel consumption rate also were recorded every 0.5 seconds. All data recorded were electronically loaded into a computerized relational data base program. This program allowed computations (e.g., of A/F and exhaust flow rate), logical searches and classification of data (e.g., selection of all periods where the A/F and the acceleration were within specified limits), and plotting of results to be performed quickly and easily. 

For classification of data vectors, the output of the net is coded by the class that corresponds with the input vector. At the end of the network training, the unknown data vectors, for example, spectra, can be assigned on the basis of the value of the output neurons. Without particular constraints, however, a pattern or class is always assigned. We already know of this disadvantage from the statistical methods of pattern recognition (Section 5.2). 

In Example 8.7, the use of a multilayer perceptron for classification of data sets in two dimensions is explored. 

Since there is no grouping of the measurements for X charts, the power of rational subgrouping is not available. The use of stratification and rational ordering of the measurements with X charts provides an alternative to rational subgrouping for individual charts. Stratification is the separation and classification of data according to selected variables or factors. Stratification on a control chart is done in two different ways. 

In conclusion it is very fortunate that this important work of selection and classification of data on palladium-catalyzed reactions originally from an industrial chemist, is now available to the scientific community. I congratulate the authors and wish them every success in their enterprise. 

LVQ procedures are intuitively clear and easy to implement. The classification of data is based on a comparison with a number of so-called prototype vectors. 

Regression analysis (black box models) i.e., statistical methods that permit the approximation of functions and the classification of data using non-parametric methods (application specific). 

Patient safety indicators have almost always been considered quantitative by nature (e.g. EUNetPas 2010 33 Kristensen et al. 2007 7). However, qualitative indicators can also provide important information on patient safety. For example, personnel s expressions of worry about patient safety issues in the organisation or quality audits that provide a written summary evaluation provide important information about patient safety - often more important than the quantitative indicators do. The problem of the quantification of, for example, adverse events and near misses is the loss of context, i.e. the loss of the story behind the incidents (Dekker 2011 Reiman and Rollenhagen 2011). Instead of data analysis it is merely a classification of data (Dekker 2011 143). Too often this classification is taken as a sufficient basis for managerial decisionmaking in situations that should require more in-depth analysis of the individual... 

Fig. 5 Principal component analysis PCA of data recorded on embryonic mice spinal cord cultures in the native state, upon application of 60-pM bicuculline and, after a washing cycle, of 25-p.M strychnine. PCI, 2 are the principal components 1 and 2. Classification of data is best if data points are close together within one cluster and clusters are well separated. For details, see text and Ref [25].

The fuzzy C-mean (FCM) approach (Udupa and Samarasekera 1996 Bezdek 1948) is able to make unsupervised classification of data in a number of clusters by identifying different tissues in an image without the use of an explicit threshold. The FCM algorithm performs a classification of image data by computing a measure of membership, called fuzzy membership, at each pixel for a specified number of classes. The fuzzy membership function, con-... 

A suitable classification of data should also be proposed. The author proposes a data classification of static configuration, operational, dynamic status data and the schedule [Faulkner 2002). This data classification may be used as the basis of an... 

Much of this research was made possible by the unfailing support from Dr. Ying Ke et al. from School of Business of Suzhou University of Science and Technology. Our appreciation as well goes to the master smdents Minyan Shen et al. from School of Business of Suzhou University of Science and Technology for the collection and classification of data. 

When data on EAs for electrode reactions in the literature are analyzed with respect to the diversity of definitions given in Table 5, some rather discouraging conclusions are reached. The majority of the data is categorized simply as = 0), because explicit indications of the WE-RE cell type and the temperature regime used are mostly missing. The only information that is certain is that all these EAs are pertinent to WEs held in an equilibrium state. Hence, a classification of data on EAs from the literature with respect to the criteria contained in Table 5 cannot be made. 

Below we summarise the previous discussions on the conditions for a reliable and unbiased classification of data on accidents and near accidents. These are favourable when ... 

There is another problem connected to the classification of data. Such classification will result in a loss of information about the detailed circumstances of the accident. These details are often essential to our understanding of why the actual accident happened, especially when we study complex human behaviour. Fortunately, the problems with biases and with loss of information run in parallel. The losses and the incident at the right side of the ILCI model of Figure 15.4, for example, are usually adequately represented by coded data. The level of details in the precursory events and conditions and in the causal factors on the left side of the model is often too high to allow for a meaningful coding. We here face a situation where rich information has to be forced into too small a frame. The person responsible for coding will then make a more or less arbitrary selection. It follows that statistical summaries of accident causes too often represent... 

Stanimirova I, Walczak B. Classification of data with missing elements and outliers. Talanta... 

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