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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. Design: 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 ;.

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those articles selected by the Surgical Oncology Society. Results: Our results showed that CCPY outperforms CC and JIF

Ling Liu, Keke Chen. Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;.

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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 ;.

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user feedback

Keke Chen, Jing Bai, Zhaohui Zheng. Ranking Function Adaptation With Boosting Trees. ACM Transactions on Information Systems. 2011 ;.

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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.

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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 ;.

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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 ;.

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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 ;.

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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 ;.

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web search ranking

Keke Chen, Jing Bai, Zhaohui Zheng. Ranking Function Adaptation With Boosting Trees. ACM Transactions on Information Systems. 2011 ;.

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what is the set of candidate &#39

Ling Liu, Keke Chen. Best K: the Critical Clustering Structures in Categorical Data. 2008 ;.

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which are generated in hierachical cluster merging processes