Introduction to clustering: Clean, prep and visualize your data
Nigerian Music scraped from Spotify - an analysis
Clustering is a type of Unsupervised Learning that presumes that a dataset is unlabelled or that its inputs are not matched with predefined outputs. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data.
Introduction
Clustering is very useful for data exploration. Let’s see if it can help discover trends and patterns in the way Nigerian audiences consume music.
✅ Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members’ clothes 🧦👕👖🩲. In data science, clustering happens when trying to analyze a user’s preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos, like a sock drawer.
In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans.
✅ Think a minute about how you might have encountered clustering ‘in the wild’, in a banking, e-commerce, or business setting.
🎓 Interestingly, cluster analysis originated in the fields of Anthropology and Psychology in the 1930s. Can you imagine how it might have been used?
Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.
✅ Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset’s privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you’d refer to a cluster Id rather than other elements of the cluster to identify it?
Getting started with clustering
🎓 How we create clusters has a lot to do with how we gather up the data points into groups. Let’s unpack some vocabulary:
🎓 ‘Transductive’ vs. ‘inductive’
Transductive inference is derived from observed training cases that map to specific test cases. Inductive inference is derived from training cases that map to general rules which are only then applied to test cases.
An example: Imagine you have a dataset that is only partially labelled. Some things are ‘records’, some ‘cds’, and some are blank. Your job is to provide labels for the blanks. If you choose an inductive approach, you’d train a model looking for ‘records’ and ‘cds’, and apply those labels to your unlabeled data. This approach will have trouble classifying things that are actually ‘cassettes’. A transductive approach, on the other hand, handles this unknown data more effectively as it works to group similar items together and then applies a label to a group. In this case, clusters might reflect ‘round musical things’ and ‘square musical things’.
🎓 ‘Non-flat’ vs. ‘flat’ geometry
Derived from mathematical terminology, non-flat vs. flat geometry refers to the measure of distances between points by either ‘flat’ (Euclidean) or ‘non-flat’ (non-Euclidean) geometrical methods.
‘Flat’ in this context refers to Euclidean geometry (parts of which are taught as ‘plane’ geometry), and non-flat refers to non-Euclidean geometry. What does geometry have to do with machine learning? Well, as two fields that are rooted in mathematics, there must be a common way to measure distances between points in clusters, and that can be done in a ‘flat’ or ‘non-flat’ way, depending on the nature of the data. Euclidean distances are measured as the length of a line segment between two points. Non-Euclidean distances are measured along a curve. If your data, visualized, seems to not exist on a plane, you might need to use a specialized algorithm to handle it.
Clusters are defined by their distance matrix, e.g. the distances between points. This distance can be measured a few ways. Euclidean clusters are defined by the average of the point values, and contain a ‘centroid’ or center point. Distances are thus measured by the distance to that centroid. Non-Euclidean distances refer to ‘clustroids’, the point closest to other points. Clustroids in turn can be defined in various ways.
Constrained Clustering introduces ‘semi-supervised’ learning into this unsupervised method. The relationships between points are flagged as ‘cannot link’ or ‘must-link’ so some rules are forced on the dataset.
An example: If an algorithm is set free on a batch of unlabelled or semi-labelled data, the clusters it produces may be of poor quality. In the example above, the clusters might group ‘round music things’ and ‘square music things’ and ‘triangular things’ and ‘cookies’. If given some constraints, or rules to follow (“the item must be made of plastic”, “the item needs to be able to produce music”) this can help ‘constrain’ the algorithm to make better choices.
🎓 ‘Density’
Data that is ‘noisy’ is considered to be ‘dense’. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or ‘crowded’ and thus this data needs to be analyzed with the appropriate clustering method. This article demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.
Deepen your understanding of clustering techniques in this Learn module
Clustering algorithms
There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let’s discuss some of the major ones:
- Hierarchical clustering. If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members’ distance to and from other objects. Hierarchical clustering is characterized by repeatedly combining two clusters.
Centroid clustering. This popular algorithm requires the choice of ‘k’, or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. K-means clustering is a popular version of centroid clustering which separates a data set into pre-defined K groups. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.
