Finding similar items: Locality-sensitive Hashing COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 4, 2020 1 Recap: Universal hashing Given a […]
Count Sketch: Finding Heavy Hitters COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 24, 2020 1 Basic definitions Let U be a universe of size n, i.e. U = {1, 2, 3, …, n}. Cash register model stream: Sequence of m elements a1,…, am where ai
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Finding similar items: Locality-sensitive Hashing COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides from http://mmds.org Designed for the textbook Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. Auckland, Aug 4, 2020 1 Recap: Universal hashing Given a
Mining Social Networks COMPSCI 753 Kaiqi Zhao Community detection § Girvan-Newman algorithm § Spectral clustering § Modularity maximization 45 Modularity § The actual fraction of the edges that fall within the given groups minus the expected fraction if edges were distributed at random § Note: The node degree and number of edges in the graph
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Mining Social Networks COMPSCI 753 Kaiqi Zhao Overview § Social network analysis § Social network introduction § Community detection § Influence maximization 1 Social networks [McAuley, Leskovec: Discovering social circles in ego networks, 2012] 2 Terminologies § Given an undirected graph 𝐺(𝑉, 𝐸), define the following terms: § Neighbor 𝑁(𝑣) : the set of nodes
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Basic Probability Definitions COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides of Kevin Wayne (https://www.cs.princeton.edu/~wayne/kleinberg-tardos/) Designed for the textbook Algorithm Design by Jon Kleinberg and Eva Tardos. Auckland, Aug 3, 2020 1 Randomized algorithms The output and running time of algorithm
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COMPSCI 753 Algorithms for Massive Data Semester 2, 2020 Tutorial 1: Locality-sensitive Hashing Ninh Pham 1 Computing MinHash signatures and estimating Jaccard sim- ilarities Given the 4 sets S1 = {c,f},S2 = {a,b},S3 = {d,e},S4 = {a,c,e}. 1. Present these sets as a binary matrix. 2. Compute the minhash values of each set using the
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Universal Hashing COMPCSI 753: Algorithms for Massive Data Ninh Pham University of Auckland Parts of this material are modifications of the lecture slides of Kevin Wayne (https://www.cs.princeton.edu/~wayne/kleinberg-tardos/) Designed for the textbook Algorithm Design by Jon Kleinberg and Eva Tardos. Auckland, Aug 3, 2020 1 Dictionary data type Dictionary problem: Given a universe U
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Misra-Gries Summary: Finding Heavy Hitters in Data Stream COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 18, 2020 1 Basic definitions Let U be a universe of size n, i.e. U = {1, 2, 3, … , n} Cash register model stream: Sequence of m elements
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Machine Learning at Scale COMPCSI 753: Algorithms for Massive Data Instructor: Ninh Pham University of Auckland Auckland, Aug 25, 2020 1 Outline An overview of machine learning Scale up supervised linear learning with Count Sketches Scale up supervised nonlinear learning with Tensor Sketches 2 General overview of ML Source: https://techgrabyte.com/10-machine-learning-algorithms-application/ 3 Supervised
程序代写代做代考 kernel Keras html algorithm deep learning Machine Learning at Scale Read More »