Unsupervised Learning

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).

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与监督学习的区别

    

无监督学习：只给出数据集，没有标注或所有数据标注相同。根据所给的数据集，将数据结构化，进行分类或者聚类。

1.聚类算法

    将数据分成多个cluster（簇？）

谷歌新闻聚类

个体基因表达聚类

2.鸡尾酒会算法

    复杂环境下声音分离

    利用octave软件

3.练习