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knowledge graph
Shreyansh Bhatt, Swati Padhee, Amit Sheth, Keke Chen, Valerie Shalin, Derek Doran, Brandon Minnery. 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)
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)
learning to rank
Keke Chen, Jing Bai, Zhaohui Zheng. Ranking Function Adaptation With Boosting Trees. ACM Transactions on Information Systems. 2011 ;.  (437.8 KB)
LMDS-MR algorithm
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.
machine learning
Keke Chen, Bharath Avusherla, Sarah Allison, Vincent Schmidt. 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
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)
machine-learned ranking
Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen. Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking. In International Conference on Computational Linguistics. 2010.  (267.85 KB)
Management and querying of encrypted data
Sagar Sharma, James Powers, Keke Chen. 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.
massive data parallel infrastructure
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.
Measurement
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.
Multidimensional Range Query
Keke Chen, Ramakanth Kavuluru, Shumin Guo. 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)
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)
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)
Outsourced Database
Keke Chen, Ramakanth Kavuluru, Shumin Guo. 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)
parallel algorithms
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.
parallel processing
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.
Privacy evaluation
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-preserving
Sagar Sharma, Keke Chen. Privacy-Preserving Boosting with Random Linear Classifiers. ACM Conference on Computer and Communications Security (CCS) 2018; 2018.  (724.46 KB)
Sagar Sharma, Keke Chen. Poster: Image Disguising for Privacy-preserving Deep Learning. ACM Conference on Computer and Communications Security (CCS) 2018; 2018.  (1.04 MB)
Privacy-preserving data mining
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)
Privacy-preserving protocols
Sagar Sharma, James Powers, Keke Chen. 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.
Random Space Encryption
Keke Chen, Ramakanth Kavuluru, Shumin Guo. 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)
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)
Scalability
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.
scalable Euclidean embedding algorithm
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.
search engine ranking
Jing Bai, Fernando Diaz, Yi Chang, Zhaohui Zheng, Keke Chen. Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking. In International Conference on Computational Linguistics. 2010.  (267.85 KB)
Security services
Sagar Sharma, James Powers, Keke Chen. 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.
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)
social media analysis
Shreyansh Bhatt, Swati Padhee, Amit Sheth, Keke Chen, Valerie Shalin, Derek Doran, Brandon Minnery. 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)
Space Situational Awareness
Keke Chen, Bharath Avusherla, Sarah Allison, Vincent Schmidt. 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
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)
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)
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)
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)

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