There are a lot of clustering algorithms to choose from. The standard
sklearn clustering suite has thirteen different clustering classes alone. So what clustering algorithms should you be using? As with every question in data science and machine learning it depends on your data. A number of those thirteen classes in
sklearn are specialised for certain tasks (such as co-clustering and bi-clustering, or clustering features instead data points). Obviously an algorithm specializing in text clustering is going to be the right choice for clustering text data, and other algorithms specialize in other specific kinds of data. Thus, if you know enough about your data, you can narrow down on the clustering algorithm that best suits that kind of data, or the sorts of important properties your data has, or the sorts of clustering you need done. All well and good, but what if you don’t know much about your data? If, for example, you are ‘just looking’ and doing some exploratory data analysis (EDA) it is not so easy to choose a specialized algorithm. So, what algorithm is good for exploratory data analysis?
Some rules for EDA clustering
To start, lets’ lay down some ground rules of what we need a good EDA clustering algorithm to do, then we can set about seeing how the algorithms available stack up.
- Don’t be wrong!: If you are doing EDA you are trying to learn and gain intuitions about your data. In that case it is far better to get no result at all than a result that is wrong. Bad results lead to false intuitions which in turn send you down completely the wrong path. Not only do you not understand your data, you misunderstand your data. This means a good EDA clustering algorithm needs to conservative in int’s clustering; it should be willing to not assign points to clusters; it should not group points together unless they really are in a cluster; this is true of far fewer algorithms than you might think.
- Intuitive Parameters: All clustering algorithms have parameters; you need some knobs to turn to adjust things. The question is: how do you pick settings for those parameters? If you know little about your data it can be hard to determine what value or setting a parameter should have. This means parameters need to be intuitive enough that you can hopefully set them without having to know a lot about your data.
- Stable Clusters: If you run the algorithm twice with a different random initialization, you should expect to get roughly the same clusters back. If you are sampling your data, taking a different random sample shouldn’t radically change the resulting cluster structure (unless your sampling has problems). If you vary the clustering algorithm parameters you want the clustering to change in a somewhat stable predictable fashion.
- Performance: Data sets are only getting bigger. You can sub-sample (but see stability), but ultimately you need a clustering algorithm that can scale to large data sizes. A clustering algorithm isn’t much use if you can only use it if you take such a small sub-sample that it is no longer representative of the data at large!
There are other nice to have features like soft clusters, or overlapping clusters, but the above desiderata is enough to get started with because, oddly enough, very few clustering algorithms can satisfy them all!