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基于 K-Means 算法的企业健康战略创新能力形成的动力机制分析。

An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm.

机构信息

School of Information Management, Xinjiang University of Finance & Economics, Urumqi, Xinjiang 830000, China.

出版信息

Comput Intell Neurosci. 2022 Jan 30;2022:3647549. doi: 10.1155/2022/3647549. eCollection 2022.

DOI:10.1155/2022/3647549
PMID:35140768
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8818436/
Abstract

Improving enterprises' independent innovation capability is critical to improving their competitive strength, industries' independent innovation capability, industries' international competitiveness, and countries' independent innovation capability, as well as building an innovative country. It is now the era of strategic innovation. Many growing businesses are focused on strategic innovation, but they overlook the issue of ensuring that strategic innovation is implemented. Management and innovation are perennial themes in the long-term development of businesses. The most pressing issue in enterprises' strategic innovation activities is how to combine their strategic direction with their superior ability and choose a strategic innovation path that is appropriate for their development. This paper uses data mining theory to establish the K-means clustering algorithm to identify the best strategic orientation of enterprise innovation strategic direction selection based on the existing advantages and capabilities of enterprises based on the analysis of the direction of enterprise strategic innovation.

摘要

提高企业自主创新能力，对于提高企业竞争实力、产业自主创新能力、产业国际竞争力和国家自主创新能力，建设创新型国家具有重要意义。现在是战略创新的时代。许多成长型企业都专注于战略创新，但却忽视了确保战略创新得以实施的问题。管理和创新是企业长期发展的永恒主题。在企业战略创新活动中，最紧迫的问题是如何将企业的战略方向与其优势能力相结合，选择一条适合企业发展的战略创新路径。本文利用数据挖掘理论建立 K-均值聚类算法，在分析企业战略创新方向的基础上，基于企业现有优势和能力，识别企业创新战略方向选择的最佳战略定位。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/5eadb0c1bc0f/CIN2022-3647549.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/5a423461f7d5/CIN2022-3647549.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/61c14715efd8/CIN2022-3647549.007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/5eadb0c1bc0f/CIN2022-3647549.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/5a423461f7d5/CIN2022-3647549.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/d19d86dadbc6/CIN2022-3647549.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/24b1c04cf6d2/CIN2022-3647549.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/2acc179e30d7/CIN2022-3647549.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/d3712ee71251/CIN2022-3647549.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/9e918a4bc3b6/CIN2022-3647549.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/61c14715efd8/CIN2022-3647549.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/61dd3d5e27b0/CIN2022-3647549.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a5c7/8818436/5eadb0c1bc0f/CIN2022-3647549.009.jpg

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本文引用的文献

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A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution.基于 K-Means 聚类、局部离群因子和多元高斯分布的活动识别自适应算法。
Sensors (Basel). 2018 Jun 6;18(6):1850. doi: 10.3390/s18061850.
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[Plaque segmentation of intracoronary optical coherence tomography images based on -means and improved random walk algorithm].
基于K均值和改进随机游走算法的冠状动脉光学相干断层扫描图像斑块分割
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2017 Jun 1;34(6):869-875. doi: 10.7507/1001-5515.201706030.
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An Initialization Method Based on Hybrid Distance for k-Means Algorithm.一种基于混合距离的k均值算法初始化方法
Neural Comput. 2017 Nov;29(11):3094-3117. doi: 10.1162/neco_a_01014. Epub 2017 Sep 28.