PPT-On the Advantage of Overlapping Clustering for

Minimizing Conductance Rohit Khandekar Guy Kortsarz and Vahab Mirrokni Outline Problem Formulation and Motivations Related Work Our Results Overlapping vs NonOverlapping Clustering. com Abstract We introduce a new approach to the problem of overlapping clustering The main idea is to formulate overlapping clustering as an optimization problem in which each data point is mapped to a small set of labels representing membership to d *: Overlapping Community Detection Using the Link-Space Transformation. Sungsu. Lim . †. , . Seungwoo. . Ryu. . ‡. , . Sejeong. Kwon. §. ,. Kyomin. Jung. ¶. , and Jae-Gil Lee. . †. † . K.H. Wong. Calibrate non-overlapping cameras using mirrors ver.6e. 1. Overview. Introduction and Problem definition. Theory. 3D . Rotation using axis-angle . representation. Axis-angle representation. 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 . *: Overlapping Community Detection Using the Link-Space Transformation. Sungsu. Lim . †. , . Seungwoo. . Ryu. . ‡. , . Sejeong. Kwon. §. ,. Kyomin. Jung. ¶. , and Jae-Gil Lee. . †. † . 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. 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. 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 . A tutorial on using mirror to calibrate non-overlapping view cameras K.H. Wong Calibrate non-overlapping cameras using mirrors ver.6e 1 Overview Introduction and Problem definition Theory 3D Rotation using axis-angle 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.. clusters. CS771: Introduction to Machine Learning. Nisheeth. K. -means algorithm: . recap. 2. Notation: . or . is a . -dim one-hot vector. (. = 1 and . mean the same).  . K-means loss function: recap. 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 .