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The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]
DBSCAN requires two parameters: ε (eps) and the minimum number of points required to form a dense region [a] (minPts). It starts with an arbitrary starting point that has not been visited. This point's ε-neighborhood is retrieved, and if it contains sufficiently many points, a cluster is started. Otherwise, the point is labeled as noise.
Chi Index [50] is an external validation index that measure the clustering results by applying the chi-squared statistic. This index scores positively the fact that the labels are as sparse as possible across the clusters, i.e., that each cluster has as few different labels as possible.
The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the record (page and the row number in the data page in page-organized engines; row offset in file-organized engines). In a non-clustered index, The physical order of the rows is not the same as the index order.
Small number of rows The cost of full table scan is less than index range scan due to small table. When query processed SELECT COUNT(*), nulls existed in the column The query is counting the number of null columns in a typical index. However, SELECT COUNT(*) can't count the number of null columns. The query is unselective The number of return ...
Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...
Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering.
There are a number of "quick styles" for each graphic that apply largely different 3D effects to the graphic, and the graphic's shapes and text can be formatted through shape styles and WordArt styles. In addition, SmartArt graphics change their colors, fonts, and effects to match the document's theme. It was included in Office since 2006 to now.