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. A Visual Framework Invites Human into the Clustering Process. In A Visual Framework Invites Human into the Clustering Process. 2003. 
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(0 bytes). VISTA: Validating and Refining Clusters via Visualization. Information Visualization. Journal for information vizualization. 2004 ;.
. VisGBT: Visually Analyzing Evolving Datasets for Adaptive Learning. In VisGBT: Visually Analyzing Evolving Datasets for Adaptive Learning. 2009.
. Validating and Refining Clusters via Visual Rendering. In Validating and Refining Clusters via Visual Rendering. 2003. (0 bytes)
(0 bytes). Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones. 25th International World Wide Web Conference (WWW 2016). Montreal, Canada: ACM; 2016. p. 603-612. 
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(367.25 KB). Trada: a Tree-Based Ranking Function Adaptation Approach. In 17th ACM Conference on Information and Knowledge Management. Napa Valley, California: ACM; 2008. p. 1143-1152. (301.46 KB)
(301.46 KB). Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems. IEEE; 2018. p. 42-51. (4.26 MB)
(4.26 MB). Towards Optimal Resource Provisioning for Running MapReduce Programs in Public Clouds. In IEEE Conference on Cloud Computing. 2011. (223.59 KB)
(223.59 KB). Towards Attack-Resilient Geometric Data Perturbation. In Towards Attack-Resilient Geometric Data Perturbation. 2007. (0 bytes)
(0 bytes). A Survey of Multiplicative Data Perturbation for Privacy Preserving Data Mining. In 2008. (244.19 KB)
(244.19 KB). SPIN: Cleaning, Monitoring, and Querying Image Streams Generated by Ground-Based Telescopes for Space Situational Awareness. 2017 ;. Citation
Keke Chen, Bharath Avusherla, Sarah Allison ,Vincent Schmidt
Data Intensive Analysis and Computing Lab
Department of Computer Science and Engineering
Wright State University, OH, USA
E-mail: {keke.chen, avusherla.2, allison.24}@wright.edu (2.5 MB)
Citation
Keke Chen, Bharath Avusherla, Sarah Allison ,Vincent Schmidt
Data Intensive Analysis and Computing Lab
Department of Computer Science and Engineering
Wright State University, OH, USA
E-mail: {keke.chen, avusherla.2, allison.24}@wright.edu
(2.5 MB). 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). 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). 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). Scalable Euclidean Embedding for Big Data. 2015 IEEE 8th International Conference on Cloud Computing. New York City, NY: IEEE; 2015. p. 773 - 780.
. A Regression Framework for Learning Ranking Functions Using Relative Relevance Judgments. In A Regression Framework for Learning Ranking Functions Using Relative Relevance Judgments. 2007. (0 bytes)
(0 bytes). RASP: Efficient Multidimensional Range Query on Attack-Resilient Encrypted Databases. In ACM Conference on Data and Application Security and Privacy (CODASPY) 2011. San Antonio, TX; 2011. (316.57 KB)
(316.57 KB). Ranking Function Adaptation With Boosting Trees. ACM Transactions on Information Systems. 2011 ;. (437.8 KB)
(437.8 KB). PrivateGraph: A Cloud-Centric System for Spectral Analysis of Large Encrypted Graphs. In Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference. Atlanta, GA, USA: IEEE; 2017.
. Privacy-Preserving Spectral Analysis of Large Graphs in Public Clouds. Asia Conference on Computer and Communications Security 2016. Xi'an, China: ACM; 2016. p. 71-82.
. Privacy-preserving Multiparty Collaborative Mining with Geometric Data Perturbation. IEEE Transactions on Parallel and Distributed Systems. 2009 ;.
. Privacy-Preserving Data Classification with Rotation Perturbation. In IEEE International Conference on Data Mining 2005 (ICDM 2005). Houston, Texas; 2005.
. Privacy-Preserving Boosting with Random Linear Classifiers. ACM Conference on Computer and Communications Security (CCS) 2018; 2018. (724.46 KB)
(724.46 KB). Poster: Image Disguising for Privacy-preserving Deep Learning. ACM Conference on Computer and Communications Security (CCS) 2018; 2018. (1.04 MB)
(1.04 MB). Knowledge Graph Enhanced Community Detection and Characterization. In Twelfth ACM International Conference on Web Search and Data Mining. Melbourne, Australia: ACM; 2019. p. 51-59. (1.47 MB)
(1.47 MB). iVIBRATE: Interactive Visualization Based Framework for Clustering Large Datasets. 2006 ;. (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). A General Boosting Method and its Application to Learning Ranking Functions for Web Search. In A General Boosting Method and its Application to Learning Ranking Functions for Web Search. 2007. (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). On Domain Similarity and Effectiveness of Adapting to Rank. In On Domain Similarity and Effectiveness of Adapting to Rank. 2009.
. Document Clustering and Ranking System for Exploring MEDLINE Citations. 2007 ;. (0 bytes)
(0 bytes). Determining the Best K for Clustering Transactional Datasets: A Coverage Density-based Approach. 2008 ;. (536 KB)
(536 KB). Detecting Change of Clustering Structure in Categorical Data Stream. In Detecting Change of Clustering Structure in Categorical Data Stream. 2006. (0 bytes)
(0 bytes). Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking. In International Conference on Computational Linguistics. 2010. (267.85 KB)
(267.85 KB). ClusterMap: Labeling Large Datasets via Visualization. In ClusterMap: Labeling Large Datasets via Visualization. 2004. (0 bytes)
(0 bytes). Cluster Rendering of Skewed Datasets via Visualization. In Cluster Rendering of Skewed Datasets via Visualization. 2003. (0 bytes)
(0 bytes). CloudVista: Visual Cluster Exploration for Extreme Scale Data in the Cloud. In Scientific and Statistical Database Management Conference. Portland OR; 2011. (557.52 KB)
(557.52 KB). Best K: the Critical Clustering Structures in Categorical Data. 2008 ;. (0 bytes)
(0 bytes). The "Best K" for Entropy-based Categorical Clustering. In The "Best K" for Entropy-based Categorical Clustering. 2005. (0 bytes)
(0 bytes). Adaptive Training Instance Selection for Cross-Domain Emotion Identification. In WI ’17. Leipzig, Germany; 2017. (710.53 KB)
(710.53 KB). Adapting Ranking Functions to User Preference. In Adapting Ranking Functions to User Preference. 2008. (0 bytes)
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