Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Self-organizing maps
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Information Retrieval
Machine Learning
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Very Large Two-Level SOM for the Browsing of Newsgroups
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Clustering categorical data: an approach based on dynamical systems
The VLDB Journal — The International Journal on Very Large Data Bases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Interactive visualization and analysis of hierarchical neural projections for data mining
IEEE Transactions on Neural Networks
k-ANMI: A mutual information based clustering algorithm for categorical data
Information Fusion
Determining the best K for clustering transactional datasets: A coverage density-based approach
Data & Knowledge Engineering
The Mahalanobis-Taguchi system - Neural network algorithm for data-mining in dynamic environments
Expert Systems with Applications: An International Journal
Modeling a dynamic design system using the Mahalanobis Taguchi system: two-step optimal algorithm
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Clustering of heterogeneously typed data with soft computing - a case study
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
| Hi-index | 0.00 |
Clustering is an important data mining problem. However, most earlier work on clustering focused on numeric attributes which have a natural ordering to their attribute values. Recently, clustering data with categorical attributes, whose attribute values do not have a natural ordering, has received more attention. A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.