ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Adaptive Meta-Clustering Approach: Combining the Information from Different Clustering Results
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
How to Control Clustering Results? Flexible Clustering Aggregation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
| Hi-index | 0.00 |
The continuing growth of data leads to major challenges for data clustering in scientific data management. Clustering algorithms must handle high data volumes/dimensionality, while users need assistance during their analyses. Ensemble clustering provides robust, high-quality results and eases the algorithm selection and parameterization. Drawbacks of available concepts are the lack of facilities for result adjustment and the missing support for result interpretation. To tackle these issues, we have already published an extended algorithm for ensemble clustering that uses soft clusterings. In this paper, we propose a novel visualization, tightly coupled to this algorithm, that provides assistance for result adjustments and allows the interpretation of clusterings for data sets of arbitrary size.