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  2. Database index - Wikipedia

    en.wikipedia.org/wiki/Database_index

    To process this statement without an index the database software must look at the last_name column on every row in the table (this is known as a full table scan). With an index the database simply follows the index data structure (typically a B-tree) until the Smith entry has been found; this is much less computationally expensive than a full ...

  3. Full table scan - Wikipedia

    en.wikipedia.org/wiki/Full_table_scan

    Full table scan occurs when there is no index or index is not being used by SQL. And the result of full scan table is usually slower that index table scan. The situation is that: the larger the table, the slower of the data returns. Unnecessary full-table scan will lead to a huge amount of unnecessary I/O with a process burden on the entire ...

  4. Query plan - Wikipedia

    en.wikipedia.org/wiki/Query_plan

    This plan table will return the cost and time for executing a query. Oracle offers two optimization approaches: CBO or Cost Based Optimization; RBO or Rule Based Optimization; RBO is slowly being deprecated. For CBO to be used, all the tables referenced by the query must be analyzed. To analyze a table, a DBA can launch code from the DBMS_STATS ...

  5. Extensible Storage Engine - Wikipedia

    en.wikipedia.org/wiki/Extensible_Storage_Engine

    Tables grow automatically in response to data creation. Tables have one or more indexes. There must be at least one clustered index for record data. When no clustered index is defined by the application, an artificial index is used which orders and clusters records by the chronological order of record insertion.

  6. Dunn index - Wikipedia

    en.wikipedia.org/wiki/Dunn_index

    The Dunn index (DI) (introduced by J. C. Dunn in 1974) is a metric for evaluating clustering algorithms. [ 1 ] [ 2 ] This is part of a group of validity indices including the Davies–Bouldin index or Silhouette index , in that it is an internal evaluation scheme, where the result is based on the clustered data itself.

  7. DBSCAN - Wikipedia

    en.wikipedia.org/wiki/DBSCAN

    Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu in 1996. [1]

  8. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri

  9. Document clustering - Wikipedia

    en.wikipedia.org/wiki/Document_clustering

    In general, there are two common algorithms. The first one is the hierarchical based algorithm, which includes single link, complete linkage, group average and Ward's method. By aggregating or dividing, documents can be clustered into hierarchical structure, which is suitable for browsing.