Self-organizing maps
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Self-Organizing Maps
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
CLOPE: a fast and effective clustering algorithm for transactional data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Unsupervised Clustering of Symbol Strings and Context Recognition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
TCSOM: Clustering Transactions Using Self-Organizing Map
Neural Processing Letters
Online algorithm for the self-organizing map of symbol strings
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
IEEE Transactions on Knowledge and Data Engineering
Determining the best K for clustering transactional datasets: A coverage density-based approach
Data & Knowledge Engineering
5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
MM '09 Proceedings of the 17th ACM international conference on Multimedia
SCALE: a scalable framework for efficiently clustering transactional data
Data Mining and Knowledge Discovery
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
After projecting high dimensional data into a two-dimension map via the SOM, users can easily view the inner structure of the data on the 2-D map. In the early stage of data mining, it is useful for any kind of data to inspect their inner structure. However, few studies apply the SOM to transactional data and the related categorical domain, which are usually accompanied with concept hierarchies. Concept hierarchies contain information about the data but are almost ignored in such researches. This may cause mistakes in mapping. In this paper, we propose an extended SOM model, the SOMCD, which can map the varied kinds of data in the categorical domain into a 2-D map and visualize the inner structure on the map. By using tree structures to represent the different kinds of data objects and the neurons' prototypes, a new devised distance measure which takes information embedded in concept hierarchies into consideration can properly find the similarity between the data objects and the neurons. Besides the distance measure, we base the SOMCD on a tree-growing adaptation method and integrate the U-Matrix for visualization. Users can hierarchically separate the trained neurons on the SOMCD's map into different groups and cluster the data objects eventually. From the experiments in synthetic and real datasets, the SOMCD performs better than other SOM variants and clustering algorithms in visualization, mapping and clustering.