PPT-Parallel Clustering of High-Dimensional Social Media Data S

Author : pamella-moone | Published Date : 2017-06-19

1 Xiaoming Gao Emilio Ferrara Judy Qiu School of Informatics and Computing Indiana University Outline Background and motivation Sequential social media stream clustering

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Parallel Clustering of High-Dimensional Social Media Data S: Transcript

1 Xiaoming Gao Emilio Ferrara Judy Qiu School of Informatics and Computing Indiana University Outline Background and motivation Sequential social media stream clustering algorithm Parallel algorithm. Alimir. . Olivettr. . Artero. , Maria Cristina . Ferreiara. de Oliveira, . Haim. . levkowitz. Information Visualization 2004. Abstract. The idea is inspired by traditional image processing techniques such as grayscale manipulation.. S. A. L. S. A. Group. http://salsahpc.indiana.edu. Principal Investigator Geoffrey Fox. Project Lead Judy Qiu. Scott . Beason. , . Jaliya. . Ekanayake. , . Thilina. . Gunarathne. , . Jong. . Youl. Explore the applicability of Microsoft technologies to real world scientific domains with a focus on data intensive applications. Expect data deluge will demand . multicore. enabled data analysis/mining. impact the perception of . excessive use of force?. Orr, T. Research Proposal. March 3, 2015. Research Problem/Question. Research Problem. There . is no central database for record keeping of excessive force incidents (Johnson, Hoyer, & Heath, 2014). Milos. . Radovanovic. , . Alexandros. . Nanopoulos. , . Mirjana. . Ivanovic. . . ICML 2009. Presented by Feng Chen. Outline. The Emergence of Hubs. Skewness. in Simulated Data. Skewness. in Real Data. Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . Supervised & Unsupervised Learning. Supervised learning. Classification. The number of classes and class labels of data elements in training data is known beforehand. Unsupervised learning. Clustering. Dimension Reduction. Student: . Seung-Hee. . Bae. Advisor: . Dr. Geoffrey C. Fox. School of Informatics and Computing. Pervasive Technology Institute. Indiana University. Thesis Defense, Jan. 17, 2012. SC 10. New Orleans, USA. Nov 17, 2010. Azure . MapReduce. AzureMapReduce. A . MapRedue. runtime for Microsoft Azure using Azure cloud services. Azure Compute. Azure BLOB storage for in/out/intermediate data storage. IoT. and Streaming Data. . IC2E Internet of Things Panel. Judy Qiu. Indiana University. Event Processing Programming Models. Query Based. Complex Event processing. SQL like languages. Programming APIs. Fuzzy . k. -means. Self-organizing maps. Evaluation of clustering results. Figures and equations from Data Clustering by . Gan. et al.. Center-based clustering. Have objective functions which define how good a solution is;. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . Xufei. Wang. , Lei Tang, . Huiji. . Gao. , and . Huan. Liu. xufei.wang@asu.edu. Arizona State University. Contact Information. Xufei. Wang. , . Huiji. . Gao. , and . Huan. Liu, Arizona State University. Scaling. Maya Smith(Winston Salem State University), Anthony Scott (Winston Salem State University), Mentor: Supun . Kamburugamuve . (Indiana University) Principal Investigator: Dr. Geoffrey Fox (Indiana University.

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