In this paper, we present a document clustering framework incorporating instance-level knowledge in the form of pairwise constraints and attribute-level knowledge in the form of keyphrases. Firstly, we initialize weights based on metric learning with pairwise constraints, then simultaneously learn two kinds of knowledge by combining the distance-based and the constraint-based approaches, finally evaluate and select clustering result based on the degree of users’ satisfaction. The experimental results demonstrate the effectiveness and potential of the proposed method
This paper discusses a new type of semi-supervised docu-ment clustering that uses partial supervisio...
The intention expresses the user’s preference for document structure division. Intention-guided docu...
In this paper, we present a constrained co-clustering approach for clustering textual documents. Our...
One reason for semi-supervised clustering fail to deliver satisfactory performance in document clust...
Most existing semi-supervised document clustering approaches are model-based clustering and can be t...
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effecti...
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effecti...
Both the instance level knowledge and the attribute level knowledge can improve clustering...
One of the key obstacles in making learning protocols realistic in applications is the need to super...
We present a novel unsupervised learning scheme that simultaneously clusters variables of several ty...
In this paper we present a technique for automatically generating hierarchical clusters of documents...
Constrained clustering is intended to improve accuracy and personalization based on the constraints ...
We present a novel unsupervised learning scheme that simultaneously clusters variables of several ty...
Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent p...
We present a novel unsupervised learning scheme that simultaneously clusters variables of several ty...
This paper discusses a new type of semi-supervised docu-ment clustering that uses partial supervisio...
The intention expresses the user’s preference for document structure division. Intention-guided docu...
In this paper, we present a constrained co-clustering approach for clustering textual documents. Our...
One reason for semi-supervised clustering fail to deliver satisfactory performance in document clust...
Most existing semi-supervised document clustering approaches are model-based clustering and can be t...
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effecti...
Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effecti...
Both the instance level knowledge and the attribute level knowledge can improve clustering...
One of the key obstacles in making learning protocols realistic in applications is the need to super...
We present a novel unsupervised learning scheme that simultaneously clusters variables of several ty...
In this paper we present a technique for automatically generating hierarchical clusters of documents...
Constrained clustering is intended to improve accuracy and personalization based on the constraints ...
We present a novel unsupervised learning scheme that simultaneously clusters variables of several ty...
Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent p...
We present a novel unsupervised learning scheme that simultaneously clusters variables of several ty...
This paper discusses a new type of semi-supervised docu-ment clustering that uses partial supervisio...
The intention expresses the user’s preference for document structure division. Intention-guided docu...
In this paper, we present a constrained co-clustering approach for clustering textual documents. Our...