Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
Example:
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Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
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Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
无监督的学习允许我们能够在很少或不知道结果应该是什么样的情况下去处理问题。
- 我们没有必要知道变量的影响也可以从数据中导出结构。
- 我们可以通过基于数据中的变量之间的关系对数据进行聚类来导出该结构。
例 :
- 聚类：收集100万个不同的基因，并找到一种自动将这些基因组合成不同变量（如寿命，位置，作用等）相似或相关的组。
- 非聚类：“鸡尾酒会算法”，让您在混乱的环境中找到结构。（即从鸡尾酒会的声音网格中识别个体声音和音乐）。