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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)
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)
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 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)
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)
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 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 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)
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)
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)
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)
there is an astounding growth in the amount of data produced and made available through the cyberspace. Efficient and high quality clustering of large datasets continues to be one of the most important problems in largescale data analysis. A commonly use
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper
Hua Yan, Keke Chen, Ling Liu. Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;.  (536 KB)
The demand on cluster analysis for categorical data continues to grow over the last decade. A well-known problem in categorical clustering is to determine the best K number of clusters. Although several categorical clustering algorithms have been develope
Ling Liu, Keke Chen. Best K: the Critical Clustering Structures in Categorical Data. 2008 ;.  (0 bytes)
simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations
Ling Liu, Keke Chen. Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;.  (0 bytes)
sampling/summarization &#161 iterative cluster analysis &#161 disk-labeling&#39
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
Privacy-preserving data mining
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2010 ;.  (1.21 MB)
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2011 ;.  (1.21 MB)
Privacy evaluation
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2011 ;.  (1.21 MB)
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2010 ;.  (1.21 MB)
our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. <br /> <b>Conclusions</b>: The text mining system studi
Ling Liu, Keke Chen. Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;.  (0 bytes)
none has satisfactorily addressed the problem of Best K for categorical clustering. Since categorical data does not have an inherent distance function as the similarity measure
Ling Liu, Keke Chen. Best K: the Critical Clustering Structures in Categorical Data. 2008 ;.  (0 bytes)
Large Data Clusters
Hua Yan, Keke Chen, Ling Liu, Zhang Yi. SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2010 ;.  (349.91 KB)
how can we efficiently and reliably determine the best Ks?
Ling Liu, Keke Chen. Best K: the Critical Clustering Structures in Categorical Data. 2008 ;.  (0 bytes)
Geometric data perturbation
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2010 ;.  (1.21 MB)
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2011 ;.  (1.21 MB)
Framework
Hua Yan, Keke Chen, Ling Liu, Zhang Yi. SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2010 ;.  (349.91 KB)
especially in large datasets. Automated algorithms and statistical methods are typically not effective in handling such particular clusters. The second problem is how to effectively label the entire data on disk (disk-labeling) without introducing additi
Keke Chen, Ling Liu. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;.  (0 bytes)
Data perturbation
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2010 ;.  (1.21 MB)
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2011 ;.  (1.21 MB)
Data mining algorithms
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2010 ;.  (1.21 MB)
Keke Chen, Ling Liu. Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2011 ;.  (1.21 MB)

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