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
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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
Using Grid-Clustering Methods in Data Classification
PARELEC '02 Proceedings of the International Conference on Parallel Computing in Electrical Engineering
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Disk-Based K-Means Clustering for Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Automatic Subspace Clustering of High Dimensional Data
Data Mining and Knowledge Discovery
Information cut for clustering using a gradient descent approach
Pattern Recognition
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A new separation measure for improving the effectiveness of validity indices
Information Sciences: an International Journal
Dampster-Shafer evidence theory based multi-characteristics fusion for clustering evaluation
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
A novel ant-based clustering algorithm using Renyi entropy
Applied Soft Computing
| Hi-index | 0.10 |
Hierarchical clustering is an important part of cluster analysis. Based on various theories, numerous hierarchical clustering algorithms have been developed, and new clustering algorithms continue to appear in the literature. It is known that both divisive and agglomerative clustering algorithms in hierarchical clustering play a pivotal role in data-based models, and have been successfully applied in clustering very large datasets. However, hierarchical clustering is parameter-sensitive. When the user has no knowledge of how to choose the input parameters, the clustering results may become undesirable. In this paper, we propose a general grid-clustering approach (GGCA) under a common assumption about hierarchical clustering. The key features of the GGCA include: (1) the combination of the divisible and the agglomerative clustering algorithms into a unifying generative framework; (2) the determination of key input parameters: an optimal grid size for the first time; and (3) the application of a two-phase merging process to aggregate all data objects. Consequently, the GGCA is a non-parametric algorithm which does not require users to input parameters, and exhibits excellent performance in dealing with not well-separated and shape-diverse clusters. Some experimental results comparing the proposed GGCA with the existing methods show the superiority of the GGCA approach.