Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Cure: an efficient clustering algorithm for large databases
Information Systems
Machine Learning
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Comparison of clustering methods for clinical databases
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Data Mining: Concepts, Models, Methods, and Algorithms
Data Mining: Concepts, Models, Methods, and Algorithms
Conjecturable knowledge discovery: A fuzzy clustering approach
Fuzzy Sets and Systems
Hi-index | 12.05 |
We present a clustering technique to discover conjecturable rules from those datasets which do not have any predefined label class. The technique uses different attributes for clustering objects and building clustering trees. The similarity between objects will be determined using k-nearest neighbors graph, which allows both numerical and categorical attributes. The technique covers the convenience of unsupervised learning as well as the ability of prediction of decision trees. The technique is an unsupervised learning, making up of two steps: (a) constructing k-nearest neighbors graph; (b) building the clustering tree (Clus-Tree). We illustrate the use of our algorithm with an example.