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  2. Document clustering - Wikipedia

    en.wikipedia.org/wiki/Document_clustering

    Although not perfect, these frequencies can usually provide some clues about the topic of the document. And sometimes it is also useful to weight the term frequencies by the inverse document frequencies. See tf-idf for detailed discussions. 5. Clustering We can then cluster different documents based on the features we have generated.

  3. 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.

  4. 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]

  5. Range query (computer science) - Wikipedia

    en.wikipedia.org/wiki/Range_query_(computer_science)

    Given a function that accepts an array, a range query (,) on an array = [,..,] takes two indices and and returns the result of when applied to the subarray [, …,].For example, for a function that returns the sum of all values in an array, the range query ⁡ (,) returns the sum of all values in the range [,].

  6. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Mark cell ‘c’ as a new cluster; Calculate the density of all the neighbors of ‘c’ If the density of a neighboring cell is greater than threshold density then, add the cell in the cluster and repeat steps 4.2 and 4.3 till there is no neighbor with a density greater than threshold density. Repeat steps 2,3 and 4 till all the cells are ...

  7. Constrained clustering - Wikipedia

    en.wikipedia.org/wiki/Constrained_clustering

    Both a must-link and a cannot-link constraint define a relationship between two data instances. Together, the sets of these constraints act as a guide for which a constrained clustering algorithm will attempt to find chunklets (clusters in the dataset which satisfy the specified constraints).

  8. B-tree - Wikipedia

    en.wikipedia.org/wiki/B-tree

    To insert a new element, search the tree to find the leaf node where the new element should be added. Insert the new element into that node with the following steps: If the node contains fewer than the maximum allowed number of elements, then there is room for the new element. Insert the new element in the node, keeping the node's elements ordered.

  9. Elbow method (clustering) - Wikipedia

    en.wikipedia.org/wiki/Elbow_method_(clustering)

    The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use. The same method can be used to choose the number of parameters in other data-driven models, such as the number of principal components to describe a data set.