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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.
Model-based clustering was first invented in 1950 by Paul Lazarsfeld for clustering multivariate discrete data, in the form of the latent class model. [ 41 ] In 1959, Lazarsfeld gave a lecture on latent structure analysis at the University of California-Berkeley, where John H. Wolfe was an M.A. student.
Evolving classification functions (ECF), evolving classifier functions or evolving classifiers are used for classifying and clustering in the field of machine learning and artificial intelligence, typically employed for data stream mining tasks in dynamic and changing environments.
Cluster construction from fuzzy memberships in two possible ways: One-to-one object-cluster assignment, to assign each object to the cluster in which it has the highest membership; One-to-multiple object-clusters assignment, to assign each object to the cluster in which it has a membership higher than a threshold.
Hard clustering: each object belongs to a cluster or not; Soft clustering (also: fuzzy clustering): each object belongs to each cluster to a certain degree (for example, a likelihood of belonging to the cluster) There are also finer distinctions possible, for example: Strict partitioning clustering: each object belongs to exactly one cluster
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Fine-grain categorization and topic codes. 810,000 Text Classification, clustering, summarization: 2002 [25] Reuters: The Reuters Corpus Volume 2 Large corpus of Reuters news stories in multiple languages. Fine-grain categorization and topic codes. 487,000 Text Classification, clustering, summarization 2005 [26] Reuters
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