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  2. Super star cluster - Wikipedia

    en.wikipedia.org/wiki/Super_star_cluster

    A super star cluster (SSC) is a very massive young open cluster that is thought to be the precursor of a globular cluster. [1] These clusters called "super" because they are relatively more luminous and contain more mass than other young star clusters. [ 2 ]

  3. List of filename extensions (S–Z) - Wikipedia

    en.wikipedia.org/wiki/List_of_filename_extensions...

    SSC: Celestia Solar System Catalog file Celestia: SSC: Stellarium Script Stellarium: ST: SMALLTALK source code file Little Smalltalk ST Structured text file STC: Celestia Star Catalog file Celestia: STC: OpenOffice.org XML spreadsheet template OpenOffice.org Calc: STD: OpenOffice.org XML drawing template OpenOffice.org Draw: STEP: Standard for ...

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

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

    When clustering text databases with the cover coefficient on a document collection defined by a document by term D matrix (of size m×n, where m is the number of documents and n is the number of terms), the number of clusters can roughly be estimated by the formula where t is the number of non-zero entries in D. Note that in D each row and each ...

  5. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/wiki/Automatic_Clustering...

    BIRCH (balanced iterative reducing and clustering using hierarchies) is an algorithm used to perform connectivity-based clustering for large data-sets. [7] It is regarded as one of the fastest clustering algorithms, but it is limited because it requires the number of clusters as an input.

  6. Consensus clustering - Wikipedia

    en.wikipedia.org/wiki/Consensus_clustering

    Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...

  7. Non-negative matrix factorization - Wikipedia

    en.wikipedia.org/wiki/Non-negative_matrix...

    That method is commonly used for analyzing and clustering textual data and is also related to the latent class model. NMF with the least-squares objective is equivalent to a relaxed form of K-means clustering: the matrix factor W contains cluster centroids and H contains cluster membership indicators.

  8. List of spreadsheet software - Wikipedia

    en.wikipedia.org/wiki/List_of_spreadsheet_software

    Both free and paid versions are available. It can handle Microsoft Excel .xls and .xlsx files, and also produce other file formats such as .et, .txt, .csv, .pdf, and .dbf. It supports multiple tabs, VBA macro and PDF converting. [10] Lotus SmartSuite Lotus 123 – for MS Windows. In its MS-DOS (character cell) version, widely considered to be ...

  9. Clustering high-dimensional data - Wikipedia

    en.wikipedia.org/wiki/Clustering_high...

    Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...