If you have paid license of Viscovery SOMine Enterprise Data, you can use Full scalle version of Innovation Maps with thousands of Keywords. In this page, we will explain how to find the map area where you are interested in based on Keywords. AS mentioned in “How to interpret the concept of selected group” you […]
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After interpret existing map, you can create new map setting different priorities to attributes. To do so, select “File | Close” to close current map. Then, In the “Explore Data” workflow, click right mouse button, and select “Create Alternative”. Then yellow “Prioritize Attributes” step appear. Dubble click this workflow step, then “Prioritize Attributes” dialog box […]
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If you have paid license of Viscovery SOMine Cluster and Classify, you can use powerful Profile Analysis features. As expained in the page “The Ugly Duckling Theorem and SOM“, the purpose of clustering is to discover new useful “segmentations”. In the philosophy of science, there is a concept called ”instrumentalism,” which is the standpoint that […]
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In science, there is an assumption that there is only one truth. For example, when measuring length or weight, the truth is assumed in the form “measured value = true value ± measurement error,” although the true value is unknowable to humans. The true value is not directly measured but is “estimated” using scientific methods. […]
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Explanation of underlying data Innovation Maps are created by using LLM Embedding Vectors to order SOM’s nodes. For now, we use OpenAI API. The Emedding Vectors have 1536 dimensions. SOM has sufficient ability to learn data with thousands of dimensions, but instead of using Embedding Vectors directly, we first reduce the dimensionality to 15 dimensions […]
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Innovation maps are distributed as default clustering or adjusted clustering so that meaningful segmentation is visible. However, do not hesitate to modify the clustering to further interpret potential concepts in the data. The default clustering in Viscovery SOMine is defined using essentially the same clustering quality measures as the Elbow method. In other words, clustering […]
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When you open a map in Viscovery SOMine, “Group Rage” is a cluster by default. A Group Range is a range of areas on the map that you want to analyze. For example, if the Group Range is set to “Cluster”, the Data Records Window displays data records (academic papers, news articles, ideas, etc.) that […]
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This tutorial will assist you on how to effectively use Mindware Innovation Maps. General Free Viewer (Viscovery SOMine Visual Explorer) You can use the SOM models provided in Innovation Maps for Free! Please download Free Viscovery SOMine Visual Explorer now. (It requires Windows 10 or higher) Viscovery SOMine Cluster and Classify The cluster and Classify […]
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After downloaded the Innovation Maps project files, you can explorer the underlying data (academic papers, news articles or business ideas) via SOM immediately. Lunch Viscovery SOMine, and select “File | Open” from the Menu. Brouse the directly you have saved the project files. Select the Viscovery SOMine Project, and then click “Open”. Or you can […]
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Self-Organizing Maps as a Concept Generation Tool Mindware Innovation Maps uses Self-Organizing Maps (SOM) to map academic papers, news articles, and ideas in high-tech fields into semantic space. We use SOM because it is the best tool for representing “Concepts”. Kohonen published the first SOM algorithm in 1982, and this is the version that is […]
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