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mhahsler
stream:Infrastructure for Data Stream Mining
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.
Maintained by Michael Hahsler. Last updated 18 days ago.
data-stream-clusteringdatastreamstream-miningcpp
39 stars 10.05 score 132 scripts 3 dependents
cran
ibelief:Belief Function Implementation
Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets <arXiv:1304.1122>, evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.
Maintained by Kuang Zhou. Last updated 4 years ago.
1 stars 2.18 score 1 dependents