Cobweb, short for “Categorization OBjectives With Evolutionary principles”, is an incremental system in machine learning for hierarchical conceptual clustering. It builds a dynamic tree-structured categorization of incoming instances based on a probability model.
Imagine you’re organizing your toy box. You might decide to group by different categories like toy type, color, or size. Now, think of a robot doing the same but with lots of information. It’s trying to make sense of new toys and put them in the right place. This is what Cobweb does: it helps a machine group or categorize new information, figuring out what it’s like, and where it fits best.
Cobweb is a part of a family of clustering algorithms in machine learning, specifically a type of conceptual clustering algorithm. Conceptual clustering contrasts from other clustering approaches by providing a concept description for each generated cluster.
Cobweb builds a classification tree as new instances are processed. At any node of the tree, it can choose to create a new category (branch), move an instance to an existing category, or move it up to the parent category based on a utility function measuring the quality of the classification tree. This utility function typically considers factors like category utility, class predictability, and attribute-value predictability.
The Cobweb model operates incrementally, starting with an empty tree and adding one instance at a time. The tree’s structure evolves as new instances are introduced, allowing the model to adapt to changes in the data distribution. This dynamic adaptability makes Cobweb useful in situations where data are arriving continuously, and a static model is insufficient.
One of the strengths of the Cobweb algorithm is that it provides a hierarchical categorization. This tree-structured categorization allows for objects to be categorized at different levels of abstraction.
The algorithm achieves a balance between the purity of the clusters—an optimally pure cluster would contain only instances of the same class—and the economy of the clustering by limiting the number of clusters, ensuring that the complexity of the tree is proportional to the true complexity in the data.
Due to its probabilistic nature, Cobweb may not always produce the same output for a given input, and its performance is subject to the specifics of the domain and dataset.
Conceptual Clustering, Supervised Learning, Unsupervised Learning, Hierarchical Clustering, Classification, Probability Model, Utility Function, Instance-Based Learning, Cluster Analysis, Decision Tree