TERMIUM Plus^®

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[A] machine learning algorithm that assumes that the data points are generated from a mixture of probability distributions [that] aims to identify the underlying probability distributions that generate the data, and uses this information to cluster the data into groups with similar properties.  1, fiche 1, Anglais, - distribution%2Dbased%20clustering%20algorithm

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Clusters are dense regions in the data space, separated by regions of the lower density of points. The DBSCAN algorithm is based on [the] intuitive notion of "clusters" and "noise. "The key idea is that for each point of a cluster, the neighborhood of a given radius has to contain at least a minimum number of points.  3, fiche 2, Anglais, - DBSCAN%20clustering%20algorithm

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DBSCAN: density-based spatial clustering of applications with noise.  3, fiche 2, Anglais, - DBSCAN%20clustering%20algorithm

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DBSCAN : regroupement spatial basé sur la densité des applications avec bruit.  3, fiche 2, Français, - algorithme%20DBSCAN

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Agglomerative hierachy clustering algorithm. This is the most common type of hierarchical clustering algorithm. It's used to group objects in clusters based on how similar they are to each other. This is a form of bottom-up clustering, where each data point is assigned to its own cluster. Then those clusters get joined together. At each iteration, similar clusters are merged until all of the data points are part of one big root cluster.  1, fiche 3, Anglais, - agglomerative%20hierarchy%20clustering%20algorithm

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[The] k-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.  1, fiche 4, Anglais, - k%2Dmeans%20algorithm

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The "means" in the k-means refers to averaging of the data; that is, finding the centroid.  1, fiche 4, Anglais, - k%2Dmeans%20algorithm

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L'algorithme des k-moyennes [...] est un algorithme couramment utilisé en analyse de données. Il permet de partitionner une collection d'objets en K classes, K étant un nombre fixé par l'utilisateur.  2, fiche 4, Français, - algorithme%20des%20k%2Dmoyennes

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Using a clustering algorithm means [giving] the algorithm a lot of input data with no labels [to] let it find any groupings in the data it can. Those groupings are called clusters.  3, fiche 5, Anglais, - clustering%20algorithm

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A conceptual clustering algorithm can search through huge amounts of data looking for multi-dimensional structures, where each structure or cluster represents a relevant concept in the problem-solving domain.  2, fiche 6, Anglais, - conceptual%20clustering%20algorithm

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An algorithm used to define regions consisting of points with similar classes of brightness. [ISO/IEC JTC N1746, 1995]  1, fiche 7, Anglais, - cluster%20algorithm

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algorithme de groupage : terme proposé dans ISO/CEI JTC N1746, 1995.  1, fiche 7, Français, - algorithme%20de%20d%C3%A9finition%20par%20luminosit%C3%A9

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K-Means Cluster Analysis Results. The k-means algorithm is a clustering method which measures the proximity between groups using the Euclidean distance between group centroids. Beginning with an initial selection of k groups, this clustering algorithm allows to locate each respondent within clusters according to his/her distance to the nearest centroid. In total, 10 cluster solutions were tested using the Environics 1995 survey data. A scree plot of the derived proximity measure(in this case the Euclidean distance between group centroids) by the cluster solution is presented in chart 1.  1, fiche 8, Anglais, - cluster%20solution

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Chart 1. K-Means Cluster Analysis: Minimum Distances Between Cluster Centers for Different Cluster Solutions  1, fiche 8, Anglais, - cluster%20solution

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Fiche 9,  Justifications,  Anglais

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The k-means algorithm is a clustering method which measures the proximity between groups using the Euclidean distance between group centroids. Beginning with an initial selection of k groups, this clustering algorithm allows to locate each respondent within clusters according to his/her distance to the nearest centroid. In total, 10 cluster solutions were tested using the Environics 1995 survey data. A scree plot of the derived proximity measure(in this case the Euclidean distance between group centroids) by the cluster solution is presented in chart 1.  1, fiche 9, Anglais, - group%20centroid

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