**SCCL: Supporting Clustering with Contrastive Learning**
Clustering is a process used in unsupervised machine learning to group data points with similar characteristics together. By clustering, we can divide a large dataset into smaller subsets that share common features. Clustering is useful in many fields, including marketing, healthcare, and biology.
Supporting Clustering with Contrastive Learning, or SCCL, is a framework to improve unsupervised clustering performance using contrastive learning. In this framework, we combined top-down clustering with bottom-up instance-wise contrastive learning to achieve better separation between clusters.
What is contrastive learning?
Contrastive learning is a process that uses two data points and tries to determine whether they are similar or not. In essence, the model learns to differentiate between two similar data points and two dissimilar data points.
Contrastive learning helps the model learn useful features by training it to differentiate between similar and dissimilar pairs. By doing this, the model learns to extract features that are useful for classification or clustering.
How does SCCL work?
SCCL leverages contrastive learning to promote better separation in unsupervised clustering. During training, the SCCL model jointly optimizes a clustering loss over the original data instances and an instance-wise contrastive loss over the associated augmented pairs.
First, SCCL performs top-down clustering which groups data points into clusters. Then, each data point is paired with a set of augmented versions of itself, and instance-wise contrastive loss is calculated between the original data point and the augmented versions of itself. By doing this, SCCL learns to extract useful features for inter-cluster distance and intra-cluster distance, resulting in better performance in unsupervised clustering.
Benefits of SCCL
SCCL has several benefits when compared to other unsupervised clustering methods:
- Better separation between clusters
- Improved feature extraction
- Incorporation of both top-down and bottom-up clustering methods
- Reduced dependence on the quality of the input data
Applications of SCCL
SCCL can be applied to many fields where unsupervised clustering is used. Here are some examples:
- Marketing: SCCL can be used to cluster customers based on their purchasing behavior, helping businesses target specific customer groups for marketing campaigns.
- Healthcare: SCCL can be used to cluster patients based on their medical history and symptoms, helping doctors diagnose diseases and provide personalized treatment plans.
- Biology: SCCL can be used to cluster organisms based on their genomic sequence, helping biologists understand the relationships between different species.
SCCL is a powerful framework that leverages contrastive learning to improve unsupervised clustering performance. Its benefits include better separation between clusters, improved feature extraction, and the incorporation of both top-down and bottom-up clustering methods. SCCL has applications in many fields, including marketing, healthcare, and biology.
