발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.4] A Model for Handling Ambiguous Entities in Fuzzy Cluster Analysis |
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| 논문투고자 |
Jong Chan Lee |
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| 논문내용 |
Fuzzy cluster analysis overcomes the limitations of the traditional Crisp method, which forcibly assigns objects to a single cluster, by representing the degree of membership to each cluster as probabilities for objects that are not completely included in any cluster. However, the excessive ambiguity of the boundaries has caused another issue by assigning probabilities even to ambiguous objects that are difficult to belong to any cluster. This paper proposes a method to classify objects that are ambiguously classified based on probabilities into a separate cluster (by adding one more to the defined k clusters), resulting in a total of k+1 clusters. It develops an objective function that reflects this approach. Additionally, the simulated annealing algorithm is used to find the global minimum of the proposed objective function. The paper explains the node structure and other details related to the algorithm and examines various implemented results to determine whether the objective function accurately reflects its intended purpose. The most crucial aspects of this paper can be broadly divided into two parts. Firstly, defining an objective function that aligns with the constraints of the problem, and secondly, constructing the implementation environment, which involves defining data structures and parameters, to apply optimization algorithms to this objective function. |
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