<p>Preserving intellectual property rights in the medical field prevents the unauthorized usage of clinical and diagnosis ideology of individuals and groups. The data leakage in reserved intellectual properties results in monotonous economic downfall globally. This is due to acquiring certain control over the medical intellectual property rights that are globally assured empirically. This article designs a Preservation-based Economic Trend Analysis (PETA) using fuzzy clustering (FC) to analyze such an economic downfall due to the issue above. This proposed analysis focuses on economic changes based on medical/clinical/diagnosis ideologies in recent years. If such changes are identified, the ideologies based on theft and data leakage are grouped using fuzzy clustering. Within each cluster, the highest and lowest possible preventive measures are suggested. The impact of the suggestions is tested over the previous economic changes data for its optimization. The fuzzy clustering is updated for the maximum preventive degree based on the optimization results. If any cluster fails to identify the maximum degree, the clusters are deformed, and new changes data are incorporated for further analysis. Considerably, the data segregation point is modified from the previous deformed cluster to prevent low preventive degrees.</p>

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Fuzzy Clustering Algorithm for Analyzing Global Economic Trends in the Preservation of Intellectual Property Rights for Medical Welfare

  • Mengyuan You

摘要

Preserving intellectual property rights in the medical field prevents the unauthorized usage of clinical and diagnosis ideology of individuals and groups. The data leakage in reserved intellectual properties results in monotonous economic downfall globally. This is due to acquiring certain control over the medical intellectual property rights that are globally assured empirically. This article designs a Preservation-based Economic Trend Analysis (PETA) using fuzzy clustering (FC) to analyze such an economic downfall due to the issue above. This proposed analysis focuses on economic changes based on medical/clinical/diagnosis ideologies in recent years. If such changes are identified, the ideologies based on theft and data leakage are grouped using fuzzy clustering. Within each cluster, the highest and lowest possible preventive measures are suggested. The impact of the suggestions is tested over the previous economic changes data for its optimization. The fuzzy clustering is updated for the maximum preventive degree based on the optimization results. If any cluster fails to identify the maximum degree, the clusters are deformed, and new changes data are incorporated for further analysis. Considerably, the data segregation point is modified from the previous deformed cluster to prevent low preventive degrees.