On Fuzzy c-Means for Data with Tolerance
Copyright policy ) Ryuichi Murata; Yasunori Endo; Hideyuki Haruyama; Sadaaki Miyamoto;
On Fuzzy c-Means for Data with Tolerance
This paper presents two new clustering algorithms which are based on the entropy regularized fuzzyc-means and can treat data with some errors. First, the tolerance is formulated and introduce into optimization problems of clustering. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of solving the problems.
- University of Tsukuba Japan
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selected citations
Citations provided by BIP!
These citations are derived from selected sources.
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! 40
Average
Top 1%
Top 10%
gold