An effective document clustering method using user-adaptable distance metrics
Hanjoon Kim, Sang‐Goo Lee · 2002
Document clustering is inherently an unsupervised learning process that organizes document (or text) data into distinct groups without depending on pre-specified knowledge. However, real-world applications, such as building a topical hierarchy for a large document collection, need to perform clustering under various kinds of constraints. This paper presents a new type of supervised clustering to organize information in a way that reflects knowledge provided by a user. As a means by which external human knowledge can be incorporated into the clustering process, a quadratic form distance metric is employed that contains a weight matrix. Also, we propose a way of representing knowledge to guide the clustering process and a variant of the gradient descent search technique to find a user-specific weight matrix under the hierarchical clustering strategy.