The quick dynamic clustering method for mixed-type data
Copyright policy ) M. B. Loginova; V. V. Ayuyev; N. N. Hlaing; A. Thura;
The quick dynamic clustering method for mixed-type data
This paper describes a new approach to high-dimensional mixed-type data clustering with missing values, which combines information on common nearest neighbors with classic between-vectors distances calculated by an original technique. The results are applied to form intersecting clusters for every missing value.
- Bauman Moscow State Technical University Russian Federation
- Moscow State Technological University Russian Federation
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP! 1
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