| 词汇 | example_english_cluster |
| 释义 | Examples of clusterThese examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. We will then examine whether the resulting clusters are associated with cohort status and relevant sociodemographic variables. Is there a group difference in the number and size of clusters produced? We can overcome the problem of data sparseness by applying not co-occurring words but co-occurring clusters to the similarity of target words. The exemplar clusters are embedded in a network of associations among words that map relations of similarity at all levels. There is evidence, especially noticeable when we turn to the more favorable clusters, that interplay between dialect and non-dialect features influences evaluative patterns. Further study needs to include the effect of thematic word clustering on vocabulary learning. In the case of 60 and 90 clusters, the results do not change significantly, even though they are better. Figure 2 does display a degree of clustering round a lower-left to upper-right axis, as expected, but the correlation is clearly quite weak. After using the same clustering algorithm, we ended up with new clusters. Note the increase in both receptor numbers per linear distance and the intensity of each punctum (clustering of receptors) with added astroglia. Thus, the target clusters were not at the ends of words. However, the reality of these clusters is questionable (see later). Standard errors are corrected for clustering among individuals and countries. Standard errors are corrected for clustering within individuals and countries. The existence of high energy ions is further confirmed by the occurrence of fusion reactions in deuterium clusters. The fact that certain onset clusters are acquired earlier than others, for example, is a melodic matter. X-ray spectra of fast ions generated from clusters by ultrashort laser pulses. When interpreting these results it should be borne in mind that the labelling of clusters is as much an art as a science. In both samples the statistical optimum appeared to be around three or four clusters. Such clustered sampling usually makes the estimates of standard errors less precise. The effect of the duration of illness was analyzed using linear regression with adjustment for clustered data. No change in the average ionization state of the clusters during this expansion is accounted for here. We have also manipulated the pole clusters in piano synthesis to achieve interesting effects in the attack transient. Similar sequences are clustered together and successively dissimilar sequences further away, without implying a hierarchy. However, the molecular data available to date identifies only small breaks amongst the sampled sequences such that these samples can minimally be formed into clusters. In particular, performance on word-final nasal+/z/ and stop+/s/ clusters is extremely high. Excluded from the analysis were cases where word-final clusters were followed by a vowel-initial word. Accuracy on bimorphemic clusters was much higher during this time. We argue that an articulatory account best explains the asymmetries in the production of word-initial and word-final clusters. Furthermore, we found no significant difference in performance on morphologically complex vs. morphologically simple stop+/s/ coda clusters. Thus, production difficulties with word-final liquid+consonant clusters are not specific to clusters. An alternative explanation for the positional asymmetry in the acquisition of clusters is morphological. All the target words in this study with word-final nasal+/z/ clusters were morphologically complex. Frequency predicts that accuracy should be high on high frequency clusters and low on low frequency clusters. We found that participants were just as likely to accurately produce word-final stop+/s/ clusters regardless of whether they were morphologically simple or morphologically complex. Based on this admittedly small data set, it would appear that production accuracy on word-final consonant clusters is not related to morphological complexity. Structural arguments would predict equal performance on word-initial /s/+stop clusters and word-final stop+/s/ clusters. On the other hand, perhaps frequency plays a role in the earlier acquisition of word-final clusters. To summarize, there is no strong evidence that these two children are more accurate at producing word-final clusters when they are morphologically complex. Moreover, it is quite clear that the authors do not treat constructions merely as expedient clusters of properties that cooccur cross-linguistically. Indeed, it seemed that the morphemes clustered at the top of the accuracy-acquisition order were more likely to enter into such memorized patterns. The subjects, then, display a variable ability to produce some onset clusters and some [l]s and [r]s correctly. Additionally, different experimental designs also make it difficult to interpret the findings about clusters that have been reported across studies. Thus, one should not expect clustering into groups of about four items for a task like recitation of the alphabet. Similarly, category clusters (discussed in sects. 2.7 and 3.4.2) imply a link between instances of the category. By looking inside the brain, it becomes possible to identify specific neuronal clusters that may be operating near different equilibria. Two main clusters stand out in the tree. Egg clusters of 10-15 eggs, < 3 days old, were used in the experiment. Higher filtering thresholds have been utilized to reduce the size of the clusters to simplify graphical representation. The clusters may actually be implying qualitative change. Species of the luteola and strenua group clustered into separate clades. Inspection of the grouping of segmentation behavior for particular clusters should serve to highlight localized differences between the adult and child participant groups. Only segmental errors were affected by the number of consonant clusters. A plot of the three canonical discriminant functions from this solution showed reasonably well separated clusters. Three types of household clusters can be distinguished in both settlements. We first assign the pieces to clusters as follows. The data can easily be plotted and possible clusters could be detected. U is called the set of exceptional pairs of clusters. If the clusters represent distinctive groups, the predictive relationship from early to later behavior should not be attenuated. Note that some boys once again clustered into the rapid group and some girls clustered into the slow group. Moreover, adults tended to respect initial consonant clusters. The control items for the digraphs were words with initial or final consonant clusters. In fact universities group their submissions in research clusters some of which might be architectural and some not. Populations were grouped according to the clusters identified. Therefore, they were less likely to coalesce into statistically significant localized clusters given the strict thresholds that we employed. Because urban-rural was a classroom level rather than student level variable, it could not be easily tested in an overall test with adjustment for clustering. However, the fivecluster solution yielded consistent clusters in each random third. Since fishermen live in a few clusters in each village, one active fisherman from each fisherman household of the randomly selected clusters was surveyed. Table 5 displays group means in the personality clusters as well as other maternal background variables. Two more motet clusters, this time not directly associated with any of the fr. 845 motets, help to explore further examples of grafting. In terms of rated involvement, the 20 problem behaviors "clustered" into 4 - 5 statistically distinct factors. Requests for more than four clusters only resulted in splintering of the four core groups, which did not benefit the analyses. In analysis of three diverse samples, two distinct clusters of highly sensitive people consistently emerged. The 249 cases with complete data were used for clustering. As noted above, the study families lived in 46 neighborhood clusters. The goal is to minimize this ratio, which can result in retaining too few clusters. Our analyses of broad clusters of internalizing and externalizing behaviors were supplemented by examination of more narrow band clusters of behavior problems. Simple scaling operations can change the monotone ordering according to size of the (dis)similarity coefficients and, hence, affect what clusters the analysis produces. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Behaviors not included in these scales did not form meaningful clusters (as indicated by exploratory factor analysis) and were dropped from further consideration. Stabilization of complex input-output functions in neural clusters formed by synapse selection. There appears to be significant convergence and recurrence in the choice and interrelation of functional elements within and between these broad clusters across religious traditions. Complex trace fossil composed of clusters of more or less horizontal unidirectionally curved elements that radiate from a narrow central area. In unidirectional flows randomization occurs in comparatively isolated vortex clusters forming the vortex street and being swept away from the body. A user can search for a (misspelled) word or for single or clusters of letters. Neither the groups nor the clusters exhibited appreciable levels of genetic divergence. In this section, we try to extend the algorithm to handle situations where the covariance matrix is not constant but varies across clusters. We also evaluate the stability of clusters with respect to synthetic remeasured array data. Even with the model-based algorithms, the number of clusters can only be estimated. The steps of assigning genes to centroids and computing new centroids are repeated until no genes are moved between clusters. Hence, it is of interest to study the performance of various clustering approaches as a function of noise level in the synthetic data. Clustering with repeated measurements produced more accurate clusters than clustering with the estimated true mean data in most cases. In a data matrix, rows usually represent objects to be clustered (typically genes), and columns usually represent features or attributes of the objects (typically experiments). In each step, similar subtrees (clusters) are merged to form the dendrogram. Assuming known categories (classes) of objects are available, we can compare clustering results by assessing the agreement of the clusters with the classes. The variants are not interspersed with one another; but form segregated clusters. Finally, there are a variety of clustering and data-mining techniques for understanding the structure of the data in other ways. At present the model only contains a limited framework supporting node- and tree-based clusters. These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. |
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