Abstract—This study describes a chance discovery method for
network that use betweeness centrality and similarity. In prior
research of chance discovery, in the chance discovery process, it
is required that analysts infer chance from visualized network,
because it is difficult that to solve problem like to guess the cause
from the data such as non-parametric problem. However, this
reasoning process has problem that chance discovery is difficult
because chance discovery depends on experience or background
knowledge of analysts. Therefore, to solve this problem, we pay
attention the mathematical element with the network, and
propose chance index that is index of network. Chance index
have three calculation methods: the sum of the reciprocal, the
product of the reciprocal, and the average reciprocal. Using the
proposal method on three kinds of data, results show that
proposal method is useful method and chance index that use
average reciprocal is most useful calculation method.
Index Terms—Network analysis, betweenness centrality,
chance discovery, data mining.
The authors are with the Osaka Prefecture University, 1-1-1, Gakuen-cho,
Naka-ku, Sakai-shi, Osaka-fu, 599-8531, Japan (e-mail:
saga@cs.osakafu-u.ac.jp).
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Cite: Yukihiro Takayama and Ryosuke Saga, "Proposal of Chance Index in Co-occurrence Visualized Network," International Journal of Knowledge Engineering vol. 1, no. 1, pp. 78-82, 2015.
