A complete list of Dr. Eick's publications can be found here.

2018

• R. Banerjee, K. Elgarroussi, S. Wang, A. Talari, Y. Zhang, and C. F. Eick, K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time. International Journal of Semantic Computing, Volume 13

• Y. Zhang, and C. F. Eick, Tracking Events in Twitter by Combining an LDA-based Approach and a Density-contour Clustering Approach. International Journal of Semantic Computing, Volume 13

• K. Elgarroussi, S. Wang, R. Banerjee and C. F. Eick, Aconcagua: A Novel Spatiotemporal Emotion Change Analysis Framework. 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, pp 54-61, November 06, 2018, Seattle, WA, USA

• R. Banerjee, K. Elgarroussi, S. Wang, Y. Zhang, and C. F. Eick, Tweet Emotion Mapping: Understanding US Emotions in Time and Space. 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), September 26 - September 28, 2018, Laguna Beach, California, USA

• Y. Zhang, and C. F. Eick, A Novel Two-Stage System for Detecting and Tracking Events in Twitter. 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), September 26 - September 28, 2018, Laguna Beach, California, USA

• S. Wang and C. F. Eick, A data mining framework for environmental and geo-spatial data analysis. International Journal of Data Science and Analytics, March 2018, Volume 5, Issue 2–3, pp 83–98

2017

• Y. Zhang and C. F. Eick, ST-COPOT: Spatio-temporal Clustering with Contour Polygon Trees. 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (ACM SIGSPATIAL 2017), Tuesday November 7 - Friday November 10, 2017, Redondo Beach, California, USA

• Y. Zhang and C. F. Eick, “Serial” versus “Parallel”: A Comparison of Spatio-Temporal Clustering Approaches. International Symposium on Methodologies for Intelligent Systems, ISMIS 2017: Foundations of Intelligent Systems pp 396-403

2016

• Y. Zhang and C. F. Eick, ST-DCONTOUR—A Serial, Density-contour Based Spatio-temporal Clustering Approach to Cluster Location Streams. 7th ACM SIGSPATIAL International Workshop on GeoStreaming. San Francisco, CA, USA, October 30th, 2016.

• F. Akdag and C. F. Eick, Interestingness Hotspot Discovery in Spatial Datasets Using a Graph-Based Approach. Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2016. 530-544.

2015

• F. Akdag and C. F. Eick, An optimized interestingness hotspot discovery framework for large gridded spatio-temporal datasets. Big Data (Big Data), 2015 IEEE International Conference on. IEEE, 2015.

• S. Wang and C. F. Eick, A Geospatial Clustering and Analysis Framework for Mining Ozone Pollution Data. Proceedings of Geocomputing 2015, Dallas, TX, USA, May 20-23, 2015.

• S. Wang T. Cai and C. F. Eick, New Clustering and Analyzing Technique for Mining Multi-source Enriched Geo-spatial Data. Proceedings of the ACM SIGMOD/PODS Workshop on Managing and Mining Enriched Geo-Spatial Data, Snowbird, Utah, USA, June 22-27, 2014.

• P. K. Amalaman and C. F. Eick, HC-edit: A Hierarchical Clustering Approach to Data Editing. International Symposium on Methodologies for Intelligent Systems. Springer International Publishing, 2015.

• P. K. Amalaman and C. F. Eick, Avalanche: A Hierarchical, Divisive Clustering Algorithm. International Workshop on Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2015.

2014

• Y. Zhang and C. F. Eick. Novel clustering and analysis techniques for mining spatio-temporal data. Proceedings of the 1st ACM SIGSPATIAL PhD Workshop. ACM, 2014.

• F. Akdag, G Chen and C. F. Eick, Creating polygon models for spatial clusters. International Symposium on Methodologies for Intelligent Systems. Springer International Publishing, 2014.

• F. Akdag, J. U. Davis and C. F. Eick, A computational framework for finding interestingness hotspots in large spatio-temporal grids. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. ACM, 2014.

• S. Wang and C. F. Eick, A polygon-based clustering and analysis framework for mining spatial datasets. GeoInformatica 18.3 (2014): 569-594.

• S. Wang T. Cai and C. F. Eick, New Clustering and Analyzing Technique for Mining Multi-source Enriched Geo-spatial Data. Proceedings of Workshop on Managing and Mining Enriched Geo-Spatial Data. ACM, 2014.

2013

• S. Wang, T Cai and C. F. Eick, New Spatiotemporal Clustering Algorithms and their Applications to Ozone Pollution. 2013 IEEE 13th International Conference on Data Mining Workshops. IEEE, 2013.

• Z Cao, S. Wang and C. F. Eick, Analyzing the composition of cities using spatial clustering. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. ACM, 2013.

• P. K. Amalaman, C. F. Eick and N Risk, Using turning point detection to obtain better regression trees. International Workshop on Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2013.

• C. F. Eick, Uncertainty management for fuzzy decision support systems. arXiv preprint arXiv:1304.2351 (2013).

• C. F. Eick, F AKdag, P. K. Amalaman and A Tadakaluru, A Framework for Discriminative Polygonal Place Scoping. COMP@ SIGSPATIAL. 2013.(2013).

2012 and Earlier Publication