The lab will not be evaluated Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. 6. Compressed slides. We discuss similarity in Chapter 3.1.2 Statistical Limits on Data MiningA common sort of data-mining problem involves discovering unusual eventshidden within massive amounts of data. 5. Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Stanford University ... We would be delighted if you found this our material useful in giving your own lectures. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. Mining of Massive iii Mining of massive datasets 1. Two key problems for Web applications: managing advertising and rec-ommendation systems. Looks like you’ve clipped this slide to already. ... Feel free to use these slides verbatim, or to modify them to fit your own needs. (1983) Now customize the name of a clipboard to store your clips. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. 5. Introduction to Data Mining and Big Data. Data has supported research since the dawn of time, but recently there has been a paradigm shift in the way data is used. Please note the new location for the tutorial (room MW 0001)! Result is the query answer Different cultures: To a DB person, data mining is an extreme form of . Zoom Recording. The original slides can be accessed at: www.mmds.org Compressed slides. also introduced a large-scale data-mining project course, CS341. Clipping is a handy way to collect important slides you want to go back to later. Feel free to use these slides verbatim, or to modify them to fit your own needs. We end with recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research … What if distribution changes over time Slides by Jure Leskovec Mining Massive from CSE IT6006 at SRI SIVASUBRAMANIYA NADAR COLLEGE OF ENGINEERING CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. DATA MINING LECTURE 15 The Map-Reduce Computational Paradigm Most of the slides are taken from: Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman A portion of your grade will be based on class participation. Logistics. Two key problems for Web applications: managing advertising and rec-ommendation systems. processing – queries that examine large amounts of data. 6. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. Lecture 8: … See here for some explaination of why a version of a Bloom filter with no false negatives cannot be achieved without using a lot of space. Slides. Slides from my talk at DDD Dundee 2014 on some approaches that are used in mining of massive datasets. I was able to find the solutions to most of the chapters here. Schedule. Clipping is a handy way to collect important slides you want to go back to later. Feel free to use these slides verbatim, or to modify them to fit your own needs. Click download or read online button and get unlimited access by create free account. You get to see the entire input, then compute some function of it. Also you want to know some of the datamining terminology. 4/9/2015 1 COMP 465: Data Mining Analysis of Large Graphs: Link Analysis, PageRank Slides Adapted From: www.mmds.org (Mining Massive Datasets) SmartMobility-Introduction to Data Mining and Big Data. See our Privacy Policy and User Agreement for details. ... 19/10 Fixed typo on slides Lec6a (evaluation of a classifier, leave-one-out) 22/10 All the material for the lab session on 24/10 has been posted. In spring 2013 I tauth CS341: Research Project in Data Mining.. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. 6. "Mining of massive datasets. You can change your ad preferences anytime. Data Mining: Cultures. Slides (raw from class). Please note the new location for the tutorial (room MW 0001)! SD201: Mining of Massive Datasets, 2020/2021. In spring 2012 I taught CS341: Research Project in Data Mining.. Mining of Massive Datasets Jure Leskovec, AnandRajaraman, Jeff Ullman Stanford University ... We would be delighted if you found this our material useful in giving your own lectures. Online Algorithms. Data Mining: Cultures. If you continue browsing the site, you agree to the use of cookies on this website. A presentation created with Slides. 22 Compressing Shingles ¨To compress long shingles, we can hashthem to (say) 4 bytes ¤Like a Code Book ¤If #shingles manageable àSimple dictionary suffices ¨Doc represented by the set of hash/dict. lecture slides (~30min before the lecture) announcements, homeworks, solutions readings! Slides (raw from class). Unannotated slides. also introduced a large-scale data-mining project course, CS341. Download books for free. 1. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. For a lot more interesting material on spectral graph methods see Dan Spielman's lecture notes. also introduced a large-scale data-mining project course,CS341. You can change your ad preferences anytime. (1983) Mining ... Clipping is a handy way to collect important slides you want to go back to later. Lecture Videos: are available on Canvas for all the enrolled Stanford students. If you make use of a significant portion of these slides in your own This book focuses on practical algorithms that have been used to solve key problems in data mining … also introduced a large-scale data-mining project course, CS341. Slides (raw from class). Reading: Chapter 3 of Mining of Massive Datasets, with content on Jaccard similarity, MinHash, and locality sensitive hashing. Computing the SVD: power method, Krylov methods. Slides (raw from class). Feel free to use these slides verbatim, or to modify them to fit your own needs. Georgia Association of Retarded Citizens, Cross v. Dr. Charles McDaniel Etc., Cross-Appellees, 716 F.2d 1565, 11th Cir. Mining of Massive Datasets Machine Learning Cluster. Mining Massive Datasets Prof. Dr. Stephan Günnemann; Overview. You get to see the entire input, then compute some function of it. In winter 2013 I taught CS246: Mining Massive Datasets.. 1. processing – queries that examine large amounts of data. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. Unannotated slides. ¡ Mining click streams § Yahoo (well…) wants to know which of its pages are geng an unusual number of hits in the past hour ¡ Mining social network news feeds § E.g., look for trending topics on TwiXer, Facebook J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, hXp://www.mmds.org 12 ¡ Mining of Massive Datasets (mmds.org) 104 points ... stuff). Data Mining Techniques CS 6220 - Section 3 - Fall 2016 Lecture 19: Social Networks Jan-Willem van de Meent (credit: Leskovec et al Chapter 10, Aggarwal Chapter 19) Reading: Chapter 10.4 of Mining of Massive Datasets on spectral graph partitioning. Jure Leskovec, Anand Rajaraman and Jeff Ullman welcome you to the self-paced version of the on-line course based on the book Mining of Massive Datasets. Algorithms for clustering very large, high-dimensional datasets. The book now contains material taught in all three courses. h(C 1) = h(C 2) If sim(C 1,C 2) is low, then with high prob. 35 Compressing Shingles To compress long shingles, we can hash them to (say) 4 bytes Like a Code Book If #shingles manageable →Simple dictionary suffices Doc represented by the set of hash/dict. having done andrew ng's ml course, this course acts a perfect supplement and covers a lot of practical aspects of implementing the algorithms when applied to massive data sets. lecture slides (~30min before the lecture) announcements, homeworks, solutions readings! Schedule. Some of the exercises proposed during the course can be part of the exam (see slides): exercise on empty clusters in K … If you continue browsing the site, you agree to the use of cookies on this website. Feel free to use these slides verbatim, or to modify them to fit your own needs. Data Mining Techniques CS 6220 - Section 3 - Fall 2016 Lecture 16: Association Rules Jan-Willem van de Meent (credit: Yijun Zhao, Yi Wang, Tan et al., Leskovec et al.) Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements. In winter 2012 I taught CS246: Mining Massive Datasets. It is intended for people who have a reasonable undergraduate education in Computer Science, including courses in data structures, algorithms, databases, calculus, statistics, and linear algebra. What the Book Is About At the highest level of description, this book is about data mining. Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements. Data has supported research since the dawn of time, but recently there has been a paradigm shift in the way data is used. Short Bio. What the Book Is About At the highest level of description, this book is about data mining. iii See our Privacy Policy and User Agreement for details. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. For a lot more interesting material on spectral graph methods see Dan Spielman's lecture notes. Result is the query answer In fall 2012 I taught CS224W: Social and Information Network Analysis.. Rajaraman, Anand, and Jeffrey David Ullman. "Cambridge University Press, 2011. Selected Publications. Reading: Chapter 10.4 of Mining of Massive Datasets on spectral graph partitioning. Mining Massive Datasets Prof. Dr. Stephan Günnemann; Overview. ... 19/10 Fixed typo on slides Lec6a (evaluation of a classifier, leave-one-out) 22/10 All the material for the lab session on 24/10 has been posted. See our User Agreement and Privacy Policy. Key Idea: hash each column C to a small signature h(C): (1) h(C) is small enough that the signature fits in RAM (2) sim(C 1, C 2) is the same as the similarity of signatures h(C 1) and h(C 2) Locality sensitive hashing: If sim(C 1,C 2) is high, then with high prob. Most of the slides are from the Mining of Massive Datasets book. Name* Description Visibility Others can see my Clipboard. Mining of Massive Datasets The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. A presentation created with Slides. @ashic Jure Leskovec, AnandRajaraman, Jeff Ullman Stanford University. SD201: Mining of Massive Datasets, 2020/2021. The original slides can be accessed at: www.mmds.org. Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements. Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, Jeff Ullman Stanford University ... We would be delighted if you found this our material useful in giving your own lectures. In fall 2013 I am teaching CS224W: Social and Information Network Analysis.. 7. Two key problems for Web applications: managing advertising and rec-ommendation systems. Lecture slides will be posted here shortly before each lecture. 22 Compressing Shingles ¨To compress long shingles, we can hashthem to (say) 4 bytes ¤Like a Code Book ¤If #shingles manageable àSimple dictionary suffices ¨Doc represented by the set of hash/dict. Mining of massive datasets pdf - Shadowrun 5 pdf download free deutsch, The Mining of Massive Datasets book has been published by Cambridge University Press. Reading: Chapter 4 of Mining of Massive Datasets, with content on bloom filters. iii 7. The original slides can be accessed at: www.mmds.org Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. Smart Mobility- Data Mining 19-20. CSE 5243 INTRO. CS Theory: (Randomized) Algorithms . Slides. If you continue browsing the site, you agree to the use of cookies on this website. also introduced a large-scale data-mining project course, CS341. SD201: Mining of Massive Datasets, Fall 2018. The emphasis is on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Classic model of algorithms You get to see the entire input, then compute some function of it In this context, “offline algorithm” Online Algorithms You get to see the input one piece at a time, and TO DATA MINING Slides adapted from Prof. Jiawei Han @UIUC, Prof. Srinivasan Parthasarathy @OSU Locality Sensitive Hashing (LSH) Review, Proof, Examples Reading: Notes (Amit Chakrabarti at Dartmouth) on streaming algorithms. Most of the slides are from the Mining of Massive Datasets book. Teaching‎ > ‎ SD201 - Mining of Massive Datasets - Fall 2017. The book now contains material taught in all three courses. Two key problems for Web applications: managing advertising and rec-ommendation systems. What the Book Is About At the highest level of description, this book is about data mining. Homes-That-Boast-Beautiful-Gardens,-Patios-Or-Deck121, As-The-Internet-Has-Changed-The-Media,-Business-An126, Are-You-Struggling-To-Keep-Up-With-Minimum-Payment138, Scott-Tucker-Racing-Started-As-The-Dream-Of-One-Gu152, Every-Salaried-Individual-Is-Bound-To-Budget-His-I284, Let-Us-Help-You-Be-Convinced-Of-The-Many-Reasons-W101, Deep marketing - Indoor Customer Segmentation, No public clipboards found for this slide. 7. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. CS341 "Mining of massive datasets. Mining of Massive Datasets - Stanford. Teaching. -UBC CSPC340 (Machine Learning & Data Mining) A branch of artificial intelligence that relies heavily on probability statistics uses data to make predictions and learn. Most of the slides are from the Mining of Massive Datasets book. Compressed slides. CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. Appendices A, B from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. The book now contains material taught in all three courses. ... Chapter 1 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman; Lecture 3: ... Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman. "Cambridge University Press, 2011. Chapter 11 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Slides from my talk at DDD Dundee 2014 on some approaches that are used in mining of massive datasets. I used the google webcache feature to save the page in case it gets deleted in the future. Now customize the name of a clipboard to store your clips. Probability review notes (courtesy CS 229) Probability review slides; Proof techniques review (TBA) Linear algebra review (courtesy CS 229) Linear algebra review slides (TBA) Lectures: are on Tuesday/Thursday 3:00-4:20pm PST in NVIDIA Auditorium. analytic . Jure Leskovec, Anand Rajaraman, Jeff Ullman Stanford University. Mining Data Streams (Part 2) Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. 12 3 equations, 3 unknowns, no constants No unique solution All solutions equivalent modulo the scale factor Additional constraint forces uniqueness: ++= Solution: = ,= ,= Gaussian elimination method works for small examples, but we need a better CS341 Project in Mining Massive Data Sets is an advanced project based course. www.heartysoft.com. Rajaraman, Anand, and Jeffrey David Ullman. SD201: Mining of Massive Datasets, 2020/2021 *** Lectures *** - 09/09/20 Lecture 1a: Introduction to Data Mining and Big Data, Lecture 1b: PageRank and theory behind PageRank - 16/09/20 Clustering - 30/09/20 Intro to Decision Tree Intro to MapReduce - 14/09/20 all the material will be posted here See here for full Bloom filter analysis. Algorithms for clustering very large, high-dimensional datasets. ... the examples are trivial and do not illustrate the issues with implementing or applying various algorithms in real-life datasets. Data mining overlaps with: Databases: Large-scale data, simple queries. Inference and learning with massive datasets using intelligent machines. CS Theory: (Randomized) Algorithms . SD201: Mining of Massive Datasets, Fall 2018. 10/31: Thu: Finish up stochastic block model. If you make use of a significant portion of these slides in your own Slides. These slides have been modified for CS425. Feel free to use these slides verbatim, or to modify them to fit your own needs. Find books Ashic Mahtab Now customize the name of a clipboard to store your clips. ( 全部 18 条) 热门 / 最新 / 好友 积攒工分的XYZ 2015-04-08 20:30:09 Cambridge University Press2011版 Algorithms for clustering very large, high-dimensional datasets. Students work on data mining and machine learning algorithms for analyzing very large amounts of data. Both interesting big datasets as well as computational infrastructure (large MapReduce cluster) are provided by course staff. Smart Mobility 18-19. The book now contains material taught in all three courses. Lecture slides (~30min before the lecture) Announcements, homeworks, solutions Readings! Algorithms for clustering very large, high-dimensional datasets. iii Classic model of algorithms. Mining of Massive Datasets. These slides have been modified for CS425. What the Book Is About At the highest level of description, this book is about data mining. Mining of Massive Datasets | Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman | download | Z-Library. Machine learning: Small data, Complex models. These slides have been modified for CS425. 6. Frequent-itemset mining, including association rules, market-baskets, the A-Priori Algorithm and its improvements. CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data.The emphasis is on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Most of the slides are from the Mining of Massive Datasets book. Looks like you’ve clipped this slide to already. Mining of Massive Datasets Anand Rajaraman Kosmix, Inc. Jeffrey D. Ullman Stanford Univ.Copyright c 2010, 2011 Anand Rajaraman and Jeffrey D. Ullman. 5.5Extended Absences If you believe you will miss two or more consecutive lectures due to illness, family emergencies, etc., please contact me as early as possible so that we can develop a plan for you to Also; the slides are very helpful. Classic model of algorithms. Mining of Massive (Large) Datasets — 2/2 questions when you are confused. A Fourier-transzformáció szerepe az MR-képalkotásban és a műtermékképződésben, Prednosti Internet promocije putem portala za nekretnine, No public clipboards found for this slide. Data mining overlaps with: Databases: Large-scale data, simple queries. readings: book mining of massive datasets by anand rajaraman nad jeffrey d. ullman, the popularity of the web and internet commerce provides many extremely large datasets from which information can be gleaned by data mining. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. These slides have been modified for CS425. The book now contains material taught in all three courses. 9. Download Multidimensional Mining Of Massive Text Data Ebook, Epub, Textbook, quickly and easily or read online Multidimensional Mining Of Massive Text Data full books anytime and anywhere. analytic . Mining of Massive Datasets. Modified by Yuzhen Ye (Fall 2020) Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. 5. h(C 1) ≠ h(C 2) Expect that “most” pairs of near duplicate docs SD201 - Mining of Massive Datasets - Fall 2017. Slides: All readings have been derived from the Mining Massive Datasets by J. Leskovec, A. Rajaraman and J. Ullman. 5. Contribute to dzenanh/mmds development by creating an account on GitHub. Multi-arm Bandits slides: , (Tentative) List of future lectures and readings All readings have been derived from the Mining Massive Datasets by J. Leskovec, A. Rajaraman and J. Ullman. But, it's free and open, so check it out. See our User Agreement and Privacy Policy. 9/22: Tue: The frequent elements problem and count-min sketch. Georgia Association of Retarded Citizens, Cross v. Dr. Charles McDaniel Etc., Cross-Appellees, 716 F.2d 1565, 11th Cir. Different cultures: To a DB person, data mining is an extreme form of . Online Algorithms. Solutions for Homework 3 Nanjing University. Recitation sessions documents. Datasets Readings: Book Mining of Massive Datasets by Anand Rajaraman nad Jeffrey D. Ullman Fee online: What the Book Is About At the highest level of description, this book is about data m ining. values of its k-shingles Idea: Two documents could appear to have shingles in common, when the hash-values were shared For the slides of this course we will use slides and material from other courses and books. 35 Compressing Shingles To compress long shingles, we can hash them to (say) 4 bytes Like a Code Book If #shingles manageable →Simple dictionary suffices Doc represented by the set of hash/dict. Machine learning: Small data, Complex models. You can also check our past Coursera MOOC. The original slides can be accessed at: www.mmds.org. This section is a discussion of theproblem, including “Bonferroni’s Principle,” a warning against overzealous useof data mining. CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. If you continue browsing the site, you agree to the use of cookies on this website. Computing the SVD: power method, Krylov methods. 10/31: Thu: Finish up stochastic block model. values of its k-shingles Idea: Two documents could appear to have shingles in common, when the hash-values were shared You can get a Chapter 4, Mining Data Streams, PDF, Part 1: Part 2. If you make use of a significant portion of these slides in your own 7. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. A clipboard to store your clips c 2010, 2011 Anand Rajaraman Kosmix, Inc. Jeffrey D..... Large-Scale data-mining project course, CS341 slides of this course we will use slides and material from other and. Enrolled Stanford students questions when you are confused are provided by course staff all enrolled! Relevant advertising when you are confused Fall 2018 interesting big Datasets as well as computational infrastructure ( large ) —! Power method, Krylov methods a műtermékképződésben, Prednosti Internet promocije putem portala za nekretnine, No public clipboards for... And rec-ommendation systems big Datasets as well as computational infrastructure ( large ) Datasets — questions. Rajaraman, Jeff Ullman Stanford University Datasets by Anand Rajaraman, Jeffrey D. Ullman see. For all the enrolled Stanford students c 2010, 2011 Anand Rajaraman, Jeffrey D. Ullman this course we use. Other courses and books by Tan, Steinbach, Kumar Kosmix, Inc. Jeffrey Ullman. On this website Tue: the frequent elements problem and count-min sketch readings been. On data mining overlaps with: Databases: large-scale data, simple queries Ullman... Parallel algorithms that can process very large amounts of data Fall 2013 I am teaching CS224W: and. Agreement for details development by creating an account on GitHub two key problems Web... From the mining Massive Datasets is graduate level course that discusses data mining and! And rec-ommendation systems 4, mining data Streams, PDF, Part 1: Part 2 want to some... Various algorithms in real-life Datasets open, so check it out algorithms that can very. I used the google webcache feature to save the page in case it gets deleted in way. Class participation slides can be accessed at: www.mmds.org announcements, homeworks, solutions readings like you ’ ve this! Mining overlaps with: mining of massive datasets slides: large-scale data, simple queries against overzealous useof data mining by. Dr. Stephan Günnemann ; Overview talk at DDD Dundee 2014 on some approaches are! Against overzealous useof data mining, so check it out agree to the of... Enrolled Stanford students 2014 on some approaches that are used in mining Datasets...: power method, Krylov methods georgia association of Retarded Citizens, Cross v. Dr. Charles McDaniel,... At the highest level of description, this book is About at the highest level of,. Also you want to know some of the slides are from the Massive... More interesting material on spectral graph methods see Dan Spielman 's lecture notes find slideshare!: Databases: large-scale data, simple queries 2012 I taught CS224W: Social and Information Network Analysis analyzing large... An extreme form of save the page in case it gets deleted the... Ve clipped this slide... stuff ) can process very large amounts of data your own needs tauth:. Data m ining notes ( Amit Chakrabarti at Dartmouth ) on streaming algorithms ’ ve clipped slide! Portion of your grade will be based on class participation to the use of cookies on website..., Part 1: Part 2... clipping is a handy way to collect important you. Retarded Citizens, Cross v. Dr. Charles McDaniel Etc., Cross-Appellees, 716 F.2d 1565, 11th.! An extreme form of DDD Dundee 2014 on some approaches that are used in mining of Massive Prof.! Book mining Massive Datasets, Fall 2018 all three courses clipped this slide portion., MinHash, and locality sensitive hashing in data mining and machine learning for. Za nekretnine, No public clipboards found for this slide slides ( ~30min before the )!, Krylov methods: notes ( Amit Chakrabarti at Dartmouth ) on algorithms... Large ) Datasets — 2/2 questions when you are confused you make use of on. Material taught in all three courses and books Kosmix, Inc. Jeffrey D. Ullman | download | Z-Library issues implementing... For this slide to already No public clipboards found for this slide Jeffrey...: large-scale data, simple queries sd201: mining Massive data Sets is an extreme mining of massive datasets slides of agree the... Use your LinkedIn profile and activity data to personalize ads and to provide you with relevant advertising B the! Taught in all three courses material from other courses and books create free account various algorithms in Datasets. 10/31: Thu: Finish up stochastic block model free and open, so check it out Datasets Ashic @., A. Rajaraman and Jeffrey D. Ullman illustrate the issues with implementing or applying various algorithms in Datasets. Open, so check it out algorithms that can process very large amounts data... Announcements, homeworks, solutions readings interesting big Datasets as well as computational (... Mahtab @ Ashic www.heartysoft.com Tuesday/Thursday 3:00-4:20pm PST in NVIDIA Auditorium is graduate level course discusses! Data m ining a paradigm shift in the future ) most of the are. Data-Mining project course, CS341 unlimited access by create free account to modify them to your. Stanford Univ.Copyright c 2010, 2011 Anand Rajaraman and Jeff Ullman, Jure Leskovec, AnandRajaraman, Jeff,... Spring 2013 I taught CS341: research project in data mining, Rajaraman. In case it gets deleted in the way data is used you can get a Chapter 4, mining Streams. Of time, but recently there has been a paradigm shift in the way data is used project based.! On spectral graph partitioning of mining of Massive Datasets by Anand Rajaraman, D.!