Chapter 29. Unsupervised Learning
This chapter will cover the details of Spark’s available tools for unsupervised learning, focusing specifically on clustering. Unsupervised learning is, generally speaking, used less often than supervised learning because it’s usually harder to apply and measure success (from an end-result perspective). These challenges can become exacerbated at scale. For instance, clustering in high-dimensional space can create odd clusters simply because of the properties of high-dimensional spaces, something referred to as the curse of dimensionality. The curse of dimensionality describes the fact that as a feature space expands in dimensionality, it becomes increasingly sparse. This means that the data needed to fill this space for statistically meaningful results increases rapidly with any increase in dimensionality. Additionally, with high dimensions comes more noise in the data. This, in turn, may cause your model to hone in on noise instead of the true factors causing a particular result or grouping. Therefore in the model scalability table, we include computational limits, as well as a set of statistical recommendations. These are heuristics and should be helpful guides, not requirements.
At its core, unsupervised learning is trying to discover patterns or derive a concise representation of the underlying structure of a given dataset.
Here are some potential use cases. At its core, these patterns might reveal topics, anomalies, or groupings ...