When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Production flow analysis - Wikipedia

    en.wikipedia.org/wiki/Production_flow_analysis

    Given a binary product-machines n-by-m matrix , rank order clustering [1] is an algorithm characterized by the following steps: . For each row i compute the number =; Order rows according to descending numbers previously computed

  3. Multi-objective optimization - Wikipedia

    en.wikipedia.org/wiki/Multi-objective_optimization

    Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.

  4. Cellular manufacturing - Wikipedia

    en.wikipedia.org/wiki/Cellular_manufacturing

    The algorithms for machine part grouping include Rank Order Clustering, Modified Rank Order Clustering, [18] and Similarity coefficients. There are also a number of mathematical models and algorithms to aid in planning a cellular manufacturing center, which take into account a variety of important variables such as, "multiple plant locations ...

  5. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    These arise when individuals rank objects in order of preference. The data are then ordered lists of objects, arising in voting, education, marketing and other areas. Model-based clustering methods for rank data include mixtures of Plackett-Luce models and mixtures of Benter models, [29] [30] and mixtures of Mallows models. [31]

  6. Automatic clustering algorithms - Wikipedia

    en.wikipedia.org/.../Automatic_Clustering_Algorithms

    Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points. [1] [needs context]

  7. 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

  8. ‘The Crossing’ by Huffington Post

    testkitchen.huffingtonpost.com/thecrossing

    Watch firsthand, in 360 video, as Susan Sarandon listens and learns about refugees' hopes, dreams and journeys

  9. UPGMA - Wikipedia

    en.wikipedia.org/wiki/UPGMA

    This algorithm is for example used in sequence alignment procedures, as it proposes one order in which the sequences will be aligned. Indeed, the guide tree aims at grouping the most similar sequences, regardless of their evolutionary rate or phylogenetic affinities, and that is exactly the goal of UPGMA [ 9 ]