원문정보
보안공학연구지원센터(IJAST)
International Journal of Advanced Science and Technology
Vol.64
2014.03
pp.101-118
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper discusses one method of clustering a high dimensional dataset using dimensionality reduction and context dependency measures (CDM). First, the dataset is partitioned into a predefined number of clusters using CDM. Then, context dependency measures are combined with several dimensionality reduction techniques and for each choice the data set is clustered again. The results are combined by the cluster ensemble approach. Finally, the Rand index is used to compute the extent to which the clustering of the original dataset (by CDM alone) is preserved by the cluster ensemble approach.
목차
Abstract
1. Introduction
2. Context-Based Proximity Measurements [19]
3. Context-dependent Cluster Structure
4. Similarity Within and Between Clusters 0
4.1. Similarity between a data item and a cluster
4.2. Similarity between clusters
5. Dimensionality Reduction Approaches
5.1. The Variance Approach (VAR)
5.2. The Combined Approach (CA)
5.3. The Direct Approach (DA)
5.4. Top-down Approach (TD)
5.5. The Bottom-up Approach (BU)
5.6. The Weighted Attribute Frequency Approach (WAF)
5.7. The Best Clustering Performance Approach (BCP)
6. Clustering in a High Dimensional Space based on Clustering in Reduced Dimensions
7. Illustrative Experimental Results
8. Conclusion
Acknowledgments
References
1. Introduction
2. Context-Based Proximity Measurements [19]
3. Context-dependent Cluster Structure
4. Similarity Within and Between Clusters 0
4.1. Similarity between a data item and a cluster
4.2. Similarity between clusters
5. Dimensionality Reduction Approaches
5.1. The Variance Approach (VAR)
5.2. The Combined Approach (CA)
5.3. The Direct Approach (DA)
5.4. Top-down Approach (TD)
5.5. The Bottom-up Approach (BU)
5.6. The Weighted Attribute Frequency Approach (WAF)
5.7. The Best Clustering Performance Approach (BCP)
6. Clustering in a High Dimensional Space based on Clustering in Reduced Dimensions
7. Illustrative Experimental Results
8. Conclusion
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보