PPT-Clustering Spatial Data Using Random Walk

David Harel and Yehuda Koren KDD 2001 Introduction Advances in database technologies resulted in huge amounts of spatial data The characteristics of spatial data pose several difficulties for clustering algorithms. Presented by Changqing Li. Mathematics. Probability. Statistics. What. . is a Random Walk?. An Intuitive understanding. : . A series of movement which direction and size are randomly decided (e.g., . Outline. Validating clustering results. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. and Semi-Supervised Learning. Longin Jan Latecki. Based on :. Xiaojin. Zhu. Semi-Supervised Learning with Graphs. PhD thesis. CMU-LTI-05-192, May 2005. Page, Lawrence and . Brin. , Sergey and . Motwani. Chapter One. Spatial Analysis. Patterns of spatially distributed points.. Correlation with environmental variables.. Interpolations and predictive models.. Spatial autocorrelation -> spatial patterns, aggregation. Based on distance to neighbors. Kaplan-Meier estimator, Moran’s I.. . the. Cluster . Structure. . of. Graphs. Christian . Sohler. joint. . work. . with. Artur . Czumaj. . and. . Pan Peng. Very. Large Networks. Examples. Social. . networks. The World Wide Web. [ & dynamic ] . point processes and big data sets . Mike . West. Department of Statistical Science. Duke University. . cellular phenotypes in vaccine adjuvant studies . Immune response studies. Eric . Feigelson. Penn State University. Arcetri. Observatory, April 2014. Background on Spatial Point Processes. Study of spatial point processes is a part of the field of spatial statistics that includes: graph, map, network data; lattice data (e.g. images); . Draft slides. Background. Consider a social graph G=(V, E), where |V|= n and |E|= m . Girvan and Newman’s algorithm for community detection runs . in O(m. 2. n) time. , and . O(n. 2. ) space. .. The . Christian Sohler. joint work with Artur Czumaj and Pan Peng. Very. Large Networks. Examples. Social. . networks. The World Wide Web. Cocitation. . graphs. Coauthorship. . graphs. Data . size. GigaByte. [ & dynamic ] . point processes and big data sets . Mike . West. Department of Statistical Science. Duke University. . cellular phenotypes in vaccine adjuvant studies . Immune response studies. Stephen J. . Hardiman. *. Capital Fund Management . France. Liran. . Katzir. Advanced Technology Labs. Microsoft Research, Israel. *Research was conducted while the author was . unaffiliated. Motivation: Social Networks. “Data is the oil of the new age”. “Data is the oil of the new age”. but, just like oil,. “unrefined data cannot really be used”. “Data is the oil of the new age”. but, just like oil,. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because . Authors: . Kexiang. Wang, . Zhifang. Sui, et al.. Organization: Peking University. Speaker: . Kexiang. Wang. E-mail: wkx@pku.edu.cn. Outline. Overview of Our Paper. Aim. We propose the adjustable affinity-preserving random walk method for generic and query-focused multi-document summarization to enforce the .