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  2. Fuzzy clustering - Wikipedia

    en.wikipedia.org/wiki/Fuzzy_clustering

    Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster.. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible.

  3. Cluster analysis - Wikipedia

    en.wikipedia.org/wiki/Cluster_analysis

    It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis refers to a family of algorithms and tasks rather than one ...

  4. Document clustering - Wikipedia

    en.wikipedia.org/wiki/Document_clustering

    Hard clustering computes a hard assignment – each document is a member of exactly one cluster. The assignment of soft clustering algorithms is soft – a document's assignment is a distribution over all clusters. In a soft assignment, a document has fractional membership in several clusters. [1]: 499 Dimensionality reduction methods can be ...

  5. Model-based clustering - Wikipedia

    en.wikipedia.org/wiki/Model-based_clustering

    An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution.

  6. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]

  7. Davies–Bouldin index - Wikipedia

    en.wikipedia.org/wiki/Davies–Bouldin_index

    The starting point for this new version of the validation index is the result of a given soft clustering algorithm (e.g. fuzzy c-means), shaped with the computed clustering partitions and membership values associating the elements with the clusters. In the soft domain, each element of the system belongs to every classes, given the membership ...

  8. The 3 Most Overpriced Cities in America, According to Gen Z ...

    www.aol.com/finance/3-most-overpriced-cities...

    Affordability is becoming a growing challenge for younger generations. Although they're often drawn to vibrant cities for their career opportunities and lifestyle perks, high housing costs make ...

  9. Calinski–Harabasz index - Wikipedia

    en.wikipedia.org/wiki/Calinski–Harabasz_index

    Similar to other clustering evaluation metrics such as Silhouette score, the CH index can be used to find the optimal number of clusters k in algorithms like k-means, where the value of k is not known a priori. This can be done by following these steps: Perform clustering for different values of k. Compute the CH index for each clustering result.

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