Conceptual clustering is a machine learning paradigm for unsupervised classification that has been defined by Ryszard S. Michalski in 1980 (Fisher 1987, Michalski 1980) and developed mainly during the 1980s. It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering methods are capable of generating hierarchical category structures; see Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis, decision tree learning, and mixture model learning.
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Conceptualclustering is a machine learning paradigm for unsupervised classification that has been defined by Ryszard S. Michalski in 1980 (Fisher 1987...
distinguished from ordinary data clustering by generating a concept description for each generated category. Conceptualclustering is closely related to fuzzy...
In computer science, data stream clustering is defined as the clustering of data that arrive continuously such as telephone records, multimedia data,...
Quantum Clustering (QC) is a class of data-clustering algorithms that use conceptual and mathematical tools from quantum mechanics. QC belongs to the family...
computer graphics. Cobweb An incremental system for hierarchical conceptualclustering. COBWEB was invented by Professor Douglas H. Fisher, currently at...
can be connected to each other as well (for example, in "fuzzy conceptualclustering" techniques used to group data, originally invented by Enrique H...
ISBN 978-0-471-01186-6. Berrios GE (1993). "European views on personality disorders: a conceptual history". Comprehensive Psychiatry. 34 (1): 14–30. doi:10.1016/0010-440X(93)90031-X...
A concept map or conceptual diagram is a diagram that depicts suggested relationships between concepts. Concept maps may be used by instructional designers...
utility is used as the category evaluation measure in the popular conceptualclustering algorithm called COBWEB (Fisher 1987). Abstraction Concept learning...
concept and concept hierarchy and mainly used as a framework for conceptualclustering and rule mining. Over the last two decades, a collection of tools...
consider the category 'spoon'. There are two distinct subgroups or conceptualclusters: spoons tend to be either large and wooden, or small and made of...
studies of cognition and subsyndromes in schizophrenia may refer to FTD as conceptual disorganization or disorganization factor. Some disagree: Unfortunately...
written using conceptual level interfaces, which reduces the impact of making physical changes to improve performance. The conceptual view provides a...
K-means Clustering" (PDF). Neural Information Processing Systems Vol.14 (NIPS 2001): 1057–1064. Chris Ding; Xiaofeng He (July 2004). "K-means Clustering via...
A single node in a deployment diagram may conceptually represent multiple physical nodes, such as a cluster of database servers. There are two types of...
plans for six city clusters had been completed in 2016, five in 2017, with eight more forthcoming for a total of 19 city cluster plans by 2020.[needs...
segments might be formed using clustering; however, note that this diagram only uses two variables, while in practice clustering employs a large number of...
example documents. Dynamic clustering based on the conceptual content of documents can also be accomplished using LSI. Clustering is a way to group documents...
In particle physics, a diquark, or diquark correlation/clustering, is a hypothetical state of two quarks grouped inside a baryon (that consists of three...
hierarchical clustering of the MDS coordinates applying Ward's method, and the computation of average ratings for each statement and cluster of statements...
cluster to support AI safety research, an online course titled "Intro to ML Safety", and a fellowship for philosophy professors to address conceptual...
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