COMP9318 Tutorial 4: Association Rule Mining Wei Wang @ UNSW Q1 I Show that if A→ B does not meet the minconf constraint, A→ BC does not either. Solution to Q1 I conf (A→ BC) = supp(ABC) supp(A) ≤ supp(AB) supp(A) = conf (A→ B) Like Apriori, we can utilize this rule when generating association […]
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1 The Tutorial Questions and Lab Projects of Week 2 Tutorial Questions 1. A server creates a port which it uses to receive requests from clients. Discuss the design issues concerning the relationship between the name of this port and the names used by clients. 2. Sun XDR marshals data by converting it into a
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Microsoft PowerPoint – Chapter 9 – Sorting Introduction to Parallel Computing George Karypis Sorting Outline Background Sorting Networks Quicksort Bucket-Sort & Sample-Sort Background Input Specification Each processor has n/p elements A ordering of the processors Output Specification Each processor will get n/p consecutive elements of the final sorted array. The “chunk” is determined by the
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Matrix Multiplication in CUDA with Shared Memory Paul Richmond This document provides explanation as to how to adapt the Lab06 starting code (link) to implement a shared memory Matrix Multiplication using CUDA. Explanation for the Starting Code Figure 1 – Naive CUDA Matrix Multiply Exercise one asks you to modify an implementation of a naive
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1 The Solutions to Tutorial Questions and Lab Projects of Week 1 Tutorial Questions 1. Give five types of hardware resource and five types of data or software resource that can usefully be shared. Give examples of their sharing as it occurs in distributed systems. Answer Hardware: CPU: compute server (executes processor-intensive applications for clients),
cse3431-lecture15-paramcurves Curves and Surfaces Curve Design Representing curves Explicit: y = f(x), z=g(x) • Cannot get multiple values for single x, infinite slopes • E.g. cannot represent a circle Implicit (2D): f(x,y) = 0 • Cannot easily compare tangent vectors at joints • Above/below test, normals from gradient Parametric: x=fx(t), y = fy(t), z= fz(t)
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Recursive Version Algorithm and Data Structure Analysis (ADSA) AVL-Trees Algorithm and Data Structure Analysis 1 Overview AVL-Trees: • Find, insert, remove Algorithm and Data Structure Analysis 2 Runtimes for Binary Search Tree Find, insert, remove: Worst case: Best case: Average case: Algorithm and Data Structure Analysis Aim: Time O(log n) in the worst case 3
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CS1010S Programming Methodology CS1010S Programming Methodology Lecture 3 Recursion, Iteration & Order of Growth 29 Aug 2018 Python Problems? cs1010s-staff@googlegroups.com 0 10 20 30 40 50 60 1 2 3 4 5 6 R 7 8 9 10 11 12 13 14 15 Le ve l Week Expected Level Progression Max Level Typical Level Minimum
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CSE 220: Systems Fundamentals I Stony Brook University Homework Assignment #2 Fall 2018 Assignment Due: October 12, 2018 by 11:59 pm Important Information about CSE 220 Homework Assignments • Read the entire homework documents twice before starting. Questions posted on Piazza whose answers are clearly stated in the documents will be given lowest priority by
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Yang_Yueqin_Description Tools Spark version: 2.2.1 Scala version: 2.11 Commands I use the time command to record the execution time. Run Time Small2.case1.txt: time spark-submit –class FrequentItemsets Yang_Yueqin_SON.jar 1 Data/small2.csv 3 Small2.case2.txt: time spark-submit –class FrequentItemsets Yang_Yueqin_SON.jar 2 Data/small2.csv 5 Beauty.case1-50.txt: time spark-submit –class FrequentItemsets Yang_Yueqin_SON.jar 1 Data/beauty.csv 50 Beauty.case2-40.txt: time spark-submit –class FrequentItemsets Yang_Yueqin_SON.jar 2
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