Algorithms for clustering data
Algorithms for clustering data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Incremental and effective data summarization for dynamic hierarchical clustering
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A methodology for analyzing SAGE libraries for cancer profiling
ACM Transactions on Information Systems (TOIS)
Online Hierarchical Clustering in a Data Warehouse Environment
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multi-step density-based clustering
Knowledge and Information Systems
HISSCLU: a hierarchical density-based method for semi-supervised clustering
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
User Oriented Hierarchical Information Organization and Retrieval
ECML '07 Proceedings of the 18th European conference on Machine Learning
Automatic Cluster Selection Using Index Driven Search Strategy
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
An AI tool for the petroleum industry based on image analysis and hierarchical clustering
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
DBStrata: a system for density-based clustering and outlier detection based on stratification
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Dynamic incremental data summarization for hierarchical clustering
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Enhancing density-based clustering: Parameter reduction and outlier detection
Information Systems
Expert system for clustering prokaryotic species by their metabolic features
Expert Systems with Applications: An International Journal
Duration discretisation for activity recognition
Technology and Health Care
| Hi-index | 0.00 |
Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a data set than partitioning algorithms. However, hierarchical clustering algorithms do not actually create clusters, but compute only a hierarchical representation of the data set. This makes them unsuitable as an automatic pre-processing step for other algorithms that operate on detected clusters. This is true for both dendrograms and reachability plots, which have been proposed as hierarchical clustering representations, and which have different advantages and disadvantages. In this paper we first investigate the relation between dendrograms and reachability plots and introduce methods to convert them into each other showing that they essentially contain the same information. Based on reachability plots, we then introduce a technique that automatically determines the significant clusters in a hierarchical cluster representation. This makes it for the first time possible to use hierarchical clustering as an automatic pre-processing step that requires no user interaction to select clusters from a hierarchical cluster representation.