BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Parallel Fuzzy c-Means Clustering for Large Data Sets
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Bayesian hierarchical clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Locating and extracting the eye in human face images
Pattern Recognition
Hierarchical document clustering using local patterns
Data Mining and Knowledge Discovery
A novel intrusion detection system based on hierarchical clustering and support vector machines
Expert Systems with Applications: An International Journal
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Survey of clustering algorithms
IEEE Transactions on Neural Networks
ASCCN: Arbitrary Shaped Clustering Method with Compatible Nucleoids
International Journal of Data Warehousing and Mining
An Efficient Method for Discretizing Continuous Attributes
International Journal of Data Warehousing and Mining
Extracting rocks from mars images with data fields
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets
International Journal of Data Warehousing and Mining
Hi-index | 0.00 |
In this paper, data field is proposed to group data objects via simulating their mutual interactions and opposite movements for hierarchical clustering. Enlightened by the field in physical space, data field to simulate nuclear field is presented to illuminate the interaction between objects in data space. In the data field, the self-organized process of equipotential lines on many data objects discovers their hierarchical clustering-characteristics. During the clustering process, a random sample is first generated to optimize the impact factor. The masses of data objects are then estimated to select core data object with nonzero masses. Taking the core data objects as the initial clusters, the clusters are iteratively merged hierarchy by hierarchy with good performance. The results of a case study show that the data field is capable of hierarchical clustering on objects varying size, shape or granularity without user-specified parameters, as well as considering the object features inside the clusters and removing the outliers from noisy data. The comparisons illustrate that the data field clustering performs better than K-means, BIRCH, CURE, and CHAMELEON.