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.

With unsupervised learning there is no feedback based on the prediction results.

Example:

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.

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).

无监督学习允许我们在不知道结果的情况下去解决问题。我们可以从那些变量对结果影响不大的数据中导出结构

我们可以通过数据之间的变量关系来对数据进行聚类，从而推导出这种结构

无监督学习对于预测结果没有反馈

ex：

聚类：收集1000000中不同的基因集合，然后找到一种方法将这些基因自动分组成相似或者相关的不同变量组，如寿命、位置、角色等

非聚类：鸡尾酒宴会算法，允许在混乱的环境中查找结构（从嘈杂的鸡尾酒宴会上分辨出讲话的声音和音乐的声音）