C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Hierarchic document classification using Ward's clustering method
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Unsupervised on-line learning of decision trees for hierarchical data analysis
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Class discovery in gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Some applications of tree-based modelling to speech and language
HLT '89 Proceedings of the workshop on Speech and Natural Language
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Automatic taxonomy generation: issues and possibilities
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Behavioural Modeling by Clustering Based on Utility Measures
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Interpretable and reconfigurable clustering of document datasets by deriving word-based rules
Proceedings of the 18th ACM conference on Information and knowledge management
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Interpretable clustering using unsupervised binary trees
Advances in Data Analysis and Classification
Data guided approach to generate multi-dimensional schema for targeted knowledge discovery
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Guiding multidimensional analysis using decision trees
CASCON '13 Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research
| Hi-index | 0.00 |
In this paper, we propose a method for hierarchical clustering based on the decision tree approach. As in the case of supervised decision tree, the unsupervised decision tree is interpretable in terms of rules, i.e., each leaf node represents a cluster, and the path from the root node to a leaf node represents a rule. The branching decision at each node of the tree is made based on the clustering tendency of the data available at the node. We present four different measures for selecting the most appropriate attribute to be used for splitting the data at every branching node (or decision node), and two different algorithms for splitting the data at each decision node. We provide a theoretical basis for the approach and demonstrate the capability of the unsupervised decision tree for segmenting various data sets. We also compare the performance of the unsupervised decision tree with that of the supervised one.