PPT-A Stable Clustering Algorithm Using the Traffic Regularity

Reporter 羅婧文 Advisor HsuehWen Tseng 1 Outline Introduction The Importance of Cluster Stability Related Work CATRBClustering Algorithm Using the Traffic Regularity of Bus Bus Recording Algorithm . Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Exploring Regularities for Improving Façade Reconstruction from Point Cloud. Supervisors. Dr.. . Ben . Gorte. Dr. .. . Sisi. . Zlatanova. Pirouz. . Nourian. . Client. Cyclomedia. Kaixuan. Zhou . University of Waikato. Possible Worlds. Day 3. TOPICS. Time without Change?. Introduction to Causation. The Regularity Theory of Causation. Problems with the Regularity Theory of Causation. Day 3. Sydney Shoemaker: “Time Without Change”. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Sushmita Roy. sroy@biostat.wisc.edu. Computational Network Biology. Biostatistics & Medical Informatics 826. Computer Sciences 838. https://compnetbiocourse.discovery.wisc.edu. Nov 3. rd. 2016. RECAP. Cynthia Sung, Dan Feldman, Daniela . Rus. October 8, 2012. Trajectory Clustering. 1. Background. Noise. Sampling frequency. Inaccurate control. SLAM . [. Ranganathan. and . Dellaert. , 2011; Cummins and Newman, 2009; . Suresh Merugu, IITR. Overview. Definition of Clustering. Existing Clustering Methods. Clustering Examples. Classification. Classification Examples. Cluster. : A collection of data objects. Similar to one another within the same cluster. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. 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 .