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this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. <br /> <b>Design</b>: A text mining system framework for automatic document clustering and ra
Ling Liu, Keke Chen. Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;.  (0 bytes)
those articles selected by the Surgical Oncology Society. <br /><b>Results</b>: Our results showed that CCPY outperforms CC and JIF
Ling Liu, Keke Chen. Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;.  (0 bytes)
traditional cluster validation techniques based on geometric shapes and density distributions are not appropriate for categorical data. In this paper
Ling Liu, Keke Chen. Best K: the Critical Clustering Structures in Categorical Data. 2008 ;.  (0 bytes)
user feedback
Keke Chen, Jing Bai, Zhaohui Zheng. Ranking Function Adaptation With Boosting Trees. ACM Transactions on Information Systems. 2011 ;.  (437.8 KB)
visual cluster exploartion
Keke Chen, Huiqi Xu, Fengguang Tian, Shumin Guo. CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Cloud. In Scientific and Statistical Database Management Conference. Portland OR; 2011.  (557.52 KB)
visualization technique
Zohreh Alavi, Sagar Sharma, Lu Zhou, Keke Chen. Scalable Euclidean Embedding for Big Data. 2015 IEEE 8th International Conference on Cloud Computing. New York City, NY: IEEE; 2015. p. 773 - 780.
we describe iVIBRATE &#161 an interactive-visualization based three-phase framework for clustering large datasets. The two main components of iVIBRATE are its VISTA visual cluster rendering subsystem
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)
we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity measure. Based on the above measure
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)
we study the entropy property between the clustering results of categorical data with different K number of clusters
Ling Liu, Keke Chen. Best K: the Critical Clustering Structures in Categorical Data. 2008 ;.  (0 bytes)
web search ranking
Keke Chen, Jing Bai, Zhaohui Zheng. Ranking Function Adaptation With Boosting Trees. ACM Transactions on Information Systems. 2011 ;.  (437.8 KB)
which demand effective solutions. The first problem is how to effectively define and validate irregularly shaped clusters
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
which invites human into the large-scale iterative clustering process through interactive visualization
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
which offers visualization-guided disk-labeling solutions that are effective in dealing with outliers
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
With continued advances in communication network technology and sensing technology
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)

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