• Journal of Korea Technology Innovation Society
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• v.21 no.3
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• pp.1127-1154
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• 2018

This paper aims to contribute to estimating the value of a patent by explaining the unobservable attributes of patent quality using observable patent citation indices. The paper first constructs patent citation data and identifies firm, university, and research institute among assignees, and then tries to explain attributes of patent quality using backward citation indices. Backward citation indices carrying information about technological sources which a given patent is based on turn out to be good predictors of forward citation indices carrying information about attributes of patent quality. Finding the functional relationships between attributes of patent quality and backward citations will lead to the improved estimation and prediction of patent value. It is found out that backward citation indices are strongly correlated the technological diversity of a patent. The paper also suggests that with whom an organization chooses to collaborate affects the attributes of patent quality.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.777-784
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• 2006

This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.