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  2. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    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.

  3. Full table scan - Wikipedia

    en.wikipedia.org/wiki/Full_table_scan

    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 ...

  4. Database index - Wikipedia

    en.wikipedia.org/wiki/Database_index

    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.

  5. Determining the number of clusters in a data set - Wikipedia

    en.wikipedia.org/wiki/Determining_the_number_of...

    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]

  6. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    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.

  7. Document clustering - Wikipedia

    en.wikipedia.org/wiki/Document_clustering

    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.

  8. Microsoft Office shared tools - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Office_shared_tools

    Microsoft Office Document Scanning (MODS) is a scanning and optical character recognition (OCR) application introduced first in Office XP. The OCR engine is based upon Nuance's OmniPage. [10] MODS is suited for creating archival copies of documents. It can embed OCR data into both MDI and TIFF files.

  9. Index (statistics) - Wikipedia

    en.wikipedia.org/wiki/Index_(statistics)

    In statistics and research design, an index is a composite statistic – a measure of changes in a representative group of individual data points, or in other words, a compound measure that aggregates multiple indicators. [1] [2] Indices – also known as indexes and composite indicators – summarize and rank specific observations. [2]

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