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  2. Mean shift - Wikipedia

    en.wikipedia.org/wiki/Mean_shift

    Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. [1] Application domains include cluster analysis in computer vision and image processing .

  3. Category:Cluster analysis algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Cluster_analysis...

    This category contains algorithms used for cluster analysis. Pages in category "Cluster analysis algorithms" ... Mean shift; N. Nearest-neighbor chain algorithm;

  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. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    Due to the expensive iterative procedure and density estimation, mean-shift is usually slower than DBSCAN or k-Means. Besides that, the applicability of the mean-shift algorithm to multidimensional data is hindered by the unsmooth behaviour of the kernel density estimate, which results in over-fragmentation of cluster tails. [16]

  6. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. [8] The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters.

  7. k-means clustering - Wikipedia

    en.wikipedia.org/wiki/K-means_clustering

    An advantage of mean shift clustering over k-means is the detection of an arbitrary number of clusters in the data set, as there is not a parameter determining the number of clusters. Mean shift can be much slower than k -means, and still requires selection of a bandwidth parameter.

  8. Step detection - Wikipedia

    en.wikipedia.org/wiki/Step_detection

    In statistics and signal processing, step detection (also known as step smoothing, step filtering, shift detection, jump detection or edge detection) is the process of finding abrupt changes (steps, jumps, shifts) in the mean level of a time series or signal.

  9. 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]