What is clustering
Clustering is a type of unsupervised learning method that does not have labels and only groups samples that look similar based on their similarity. It is different from classification - the categories of classification are pre-defined and annotated; The "groups" of clustering rely on algorithms to discover themselves, and often require people to interpret what each group means and whether it has value.
What problem does it need to solve
There is a large amount of unlabeled data in reality: user behavior logs, products, images, documents. Clustering can help us discover the internal structure of data without a standard answer - which users are similar, which documents are discussing the same topic, and which device states are similar. Therefore, it is commonly used for exploratory analysis, grouping, deduplication, compression, and data visualization, and is also commonly used as a preprocessing step for other tasks.
How to measure 'similarity'
The premise of clustering is to define the distance (or similarity) between samples. Euclidean distance is commonly used for numerical features; Text, images, etc. are usually first converted into vectors (embeddings), and then cosine similarity is used. Choosing the right distance metric directly determines whether the clustering results are meaningful, which is also a typical scenario where "features are more important than algorithms".
Main algorithms
1. K-means: Pre specify the number of clusters K, repeat two steps - assign each point to the nearest cluster center, and then recalculate the center of each cluster. It is simple and fast, but requires guessing K first, and is sensitive to initial centers and outliers, only good at clusters that are "spherical and of similar size".
2. Hierarchical Clustering: Continuously merging the two nearest clusters from bottom to top (or splitting them from top to bottom) to obtain a "clustering tree", presented in a tree diagram. The advantage is that there is no need to pre-set K, and grouping at different granularities can be observed.
3. Density based methods, such as DBSCAN, treat "high-density" connected regions as clusters and discover clusters of any shape. They can also label sparse regions as noise. It does not require specifying the number of clusters, but two parameters, "neighborhood radius" and "minimum number of points", need to be adjusted.
4. Gaussian Mixture Model (GMM): Assuming that the data is generated by a mixture of several Gaussian distributions, the probability of each point belonging to each cluster is given in a probabilistic manner, which can be seen as a soft clustering version of K-means.
How to evaluate the clustering effect
It is difficult to evaluate without labels, and internal indicators such as silhouette coefficient (Silhouette) and CH index can only be used to check whether the clusters are tight enough and whether the clusters are far enough. If there happens to be a portion of the labels, external indicators such as the Rand Index (ARI) can be adjusted. It should be noted that any indicators are only for reference, and the clustering results should ultimately be returned to the business to determine whether the distribution is reasonable.
Common pitfalls
The dimensional differences of different features will dominate the distance, usually requiring standardization first; K-means is very sensitive to the value of K, and is often assisted by the "elbow method" or contour coefficient for selection; In high-dimensional data, distances tend to become more uniform ("curse of dimensionality"), often resulting in better clustering effects after dimensionality reduction.
Typical Applications
User segmentation and fine-grained operation, document topic clustering, image segmentation and compression, pre-processing of anomaly detection, cold start of recommendation systems, gene expression grouping in bioinformatics, etc. are all common battlefields for clustering.
one-sentence summary
Clustering allows machines to find structures on their own in data without standard answers. It usually doesn't have a single correct answer, how to explain and utilize these groups is the true source of value.
【 Reference source 】 Comprehensive compilation of published textbooks and industry materials, including official documents on clustering algorithms from scikit learn, as well as relevant public papers on classic methods such as K-means, DBSCAN, and hierarchical clustering.