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- W61637833 abstract "Identifying likeness between events is one of the fundamental necessities in machine learning and data mining techniques. Though grouping of events usually happens on their proximity in Euclidean space or the degree of similarity or the extent of linear dependence, certain applications like keyword and document clustering, phylogenetic profiling and feature selection tend to yield better results if events are grouped based on their mutual association. This paper presents a metric, the Bidirectional Association Similarity (BiAS) to quantify the degree of mutual association between a pair of events. We put forward generalized formulation to compute BiAS and establish unidirectional correspondence with the Jaccard and the cosine similarities. The measure can be suitably incorporated with clustering algorithms in grouping mutually associative events with adding precision to the discovered knowledge." @default.
- W61637833 created "2016-06-24" @default.
- W61637833 creator A5000495330 @default.
- W61637833 date "2013-01-01" @default.
- W61637833 modified "2023-09-24" @default.
- W61637833 title "BiAS: A Theme Metric to Model Mutual Association" @default.
- W61637833 cites W2009303531 @default.
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- W61637833 doi "https://doi.org/10.1007/978-3-642-45062-4_8" @default.
- W61637833 hasPublicationYear "2013" @default.
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