Library
. A Survey of Multiplicative Data Perturbation for Privacy Preserving Data Mining. In 2008. 
(244.19 KB)
(244.19 KB). The "Best K" for Entropy-based Categorical Clustering. In The "Best K" for Entropy-based Categorical Clustering. 2005. 
(0 bytes)
(0 bytes). Cluster Rendering of Skewed Datasets via Visualization. In Cluster Rendering of Skewed Datasets via Visualization. 2003. (0 bytes)
(0 bytes). ClusterMap: Labeling Large Datasets via Visualization. In ClusterMap: Labeling Large Datasets via Visualization. 2004. (0 bytes)
(0 bytes). Detecting Change of Clustering Structure in Categorical Data Stream. In Detecting Change of Clustering Structure in Categorical Data Stream. 2006. (0 bytes)
(0 bytes). Efficiently Clustering Transactional Data with Weighted Coverage Density. In Efficiently Clustering Transactional Data with Weighted Coverage Density. 2006. (0 bytes)
(0 bytes). Privacy-Preserving Data Classification with Rotation Perturbation. In IEEE International Conference on Data Mining 2005 (ICDM 2005). Houston, Texas; 2005.
. Space Adaptation: Privacy-preserving Multiparty Collaborative Mining with Geometric Perturbation. In Space Adaptation: Privacy-preserving Multiparty Collaborative Mining with Geometric Perturbation. 2007. (0 bytes)
(0 bytes). Towards Attack-Resilient Geometric Data Perturbation. In Towards Attack-Resilient Geometric Data Perturbation. 2007. (0 bytes)
(0 bytes). Validating and Refining Clusters via Visual Rendering. In Validating and Refining Clusters via Visual Rendering. 2003. (0 bytes)
(0 bytes). A Visual Framework Invites Human into the Clustering Process. In A Visual Framework Invites Human into the Clustering Process. 2003. (0 bytes)
(0 bytes). Best K: the Critical Clustering Structures in Categorical Data. 2008 ;. (0 bytes)
(0 bytes). Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;. (536 KB)
(536 KB). Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;. (0 bytes)
(0 bytes). Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2010 ;. (1.21 MB)
(1.21 MB). Geometric Data Perturbation for Privacy Preserving Outsourced Data Mining. Journal of Knowledge and Information Systems (KAIS). 2011 ;. (1.21 MB)
(1.21 MB). HE-Tree: a Framework for Detecting Changes in Clustering Structure for Categorical Data Streams. 2009 ;.
. iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;. (0 bytes)
(0 bytes). Privacy-preserving Multiparty Collaborative Mining with Geometric Data Perturbation. IEEE Transactions on Parallel and Distributed Systems. 2009 ;.
. SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2009 ;. (349.91 KB)
(349.91 KB). SCALE: a Scalable Framework for Efficiently Clustering Large Transactional Data. Journal of Data Mining and Knowledge Discovery (DMKD). 2010 ;. (349.91 KB)
(349.91 KB). VISTA: Validating and Refining Clusters via Visualization. Information Visualization. Journal for information vizualization. 2004 ;.