A Mean Field Annealing Algorithm for Fuzzy Clustering

Song, C.; Jeong, Jin-Ku; Seo, Dong-Hun; Lee, Won Don

doi:10.1109/fskd.2007.55

Cited by 1 publication

(2 citation statements)

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“…Here, the number Gi represents the coordinates of the cluster center of the digit i. In other words, it shows that the distances between the pair of digits (1,5), (4,6), (2,3), and (7,8) are closer than other cases. And it means that the shape of the digit 4 is close to that of 6, and so on, which coincides with the intuitive human judgment.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…So, to realize the equation, one-variable SSA is used [6][7] [8]. The difference between SSA and onevariable SSA is that in the SSA, the perturbed state of all the n spins is regarded as a candidate solution, but in one-variable SSA, the average of the perturbed state of only one spin is regarded as an equilibrium average of that spin under the field of the rest of the system.…”

Section: One Variable Stochastic Simulated Annealing (Ovssa)mentioning

confidence: 99%

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Visualizing a multi-dimensional data set in a lower dimensional space

Seo

Lee

2008

2008 First International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)

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This paper presents a method of visualizing a multi-dimensional data set into a lower dimensional space, especially into a two-dimensional space, so that people can intuitively conceive the relations or the distance between the entities of the data. Kullback-Leibler divergence is used as the measure to evaluate the distance between the vectors of the probability distribution. The measured distance values are used to find the corresponding coordinates of the entities in a lower dimensional space. Here, the one variable stochastic simulated annealing (OVSSA) is employed as the optimization technique. Experiments show that this is a plausible way of visualizing the multi-dimensional data, letting people see the relations among the entities intuitively.

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