Lecture 6 – 1 DSCI 4520/5240 DATA MINING DATA MINING AT WORK: Telstra Mobile Combats Churn with SAS® As Australia’s largest mobile service provider, Telstra Mobile is reliant on highly effective churn management. In most industries the cost of retaining a customer, subscriber or client is substantially less than the initial cost of obtaining that […]
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Presentation_final Combinational Collaborative Filtering: An Approach For Personalised, Contextually Relevant Product Recommendation Baskets Research Project – Jai Chopra (338852) Dr Wei Wang (Supervisor) Dr Yifang Sun (Assessor) • Recommendation engines are now heavily used online • 35% of Amazon purchases are from algorithms • We would like to extend on current implementations and provide some
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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, Jun. 2016 3286 Copyright ⓒ2016 KSII This work is supported in part by National Basic Research Program of China (No.2012CB316400), National Natural Science Foundation of China (No. 61210006, 61402034), the Program for Changjiang Scholars, Innovative Research Team in University under Grant IRT201206, Beijing Natural
Inf553 – Foundations and Applications of Data Mining Fall 2018 The 4nd USC Informatics Data Mining Competition Starting Date: Oct 12 Friday 2018 End Date: Nov 29 Thursday 2018 11:59 PM PST 1 Competition Overview This competition is based on the assignment 3, recommendation system. You need to keep improving the performance of your recommendation
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1.dvi D RA FT Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Daniel Jurafsky & James H. Martin. Copyright c© 2006, All rights reserved. Draft of June 25, 2007. Do not cite without permission. 1 INTRODUCTION Dave Bowman: Open the pod bay doors, HAL. HAL: I’m sorry Dave,
MapReduce and Hadoop Lecture 2: MapReduce and Frequent Itemsets Prof. Michael R. Lyu Computer Science & Engineering Dept. The Chinese University of Hong Kong 1 CMSC5741 Big Data Tech. & Apps. 1 Outline Introduction The Hadoop Distributed File System (HDFS) MapReduce Hadoop Hadoop Streaming Problems Suited for MapReduce TensorFlow Frequent Itemsets Conclusion 2 Introduction Much
EM623-Week6 Carlo Lipizzi clipizzi@stevens.edu SSE 2016 Machine Learning and Data Mining Decision Trees: definitions, algorithms, applications, optimizations and implementation using R/Rattle Machine learning and our focus • Like human learning from past experiences • A computer does not have “experiences” • A computer system learns from data, which represent some “past experiences” of an application
CS373 Data Mining and� Machine Learning� Lecture 8 Jean Honorio Purdue University (originally prepared by Tommi Jaakkola, MIT CSAIL) Today’s topics • Ensembles and Boosting - ensembles, relation to feature selection - myopic forward-fitting and boosting - understanding boosting Ensembles • An ensemble classifier combines a set of m “weak” base learners into a “strong” ensemble� � � � �
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Chapter 1: Introduction COMP9313: Big Data Management Lecturer: Xin Cao Course web site: http://www.cse.unsw.edu.au/~cs9313/ 3.‹#› 1 Chapter 3: MapReduce II 3.‹#› Overview of Previous Lecture Motivation of MapReduce Data Structures in MapReduce: (key, value) pairs Map and Reduce Functions Hadoop MapReduce Programming Mapper Reducer Combiner Partitioner Driver 3.‹#› Combiner Function To minimize the data transferred
Sample_FinalPresentation VALUE OF A COLLEGE DEGREE A DATA MINING APPROACH EM623 DATA SCIENCE AND KNOWLEDGE DISCOVERY JASON WONG INTRODUCTION • It seems like everyone these days is going to school, in school, or plans to go back to school • Why? • Self-improvement • Cultural norm • Economic mobility • One of the main reasons