PPT-Information Retrieval & Data Mining
Author : wellific | Published Date : 2020-06-23
Universität des Saarlandes Saarbrücken Winter Semester 201112 The Course Lecturers Teaching Assistants October 18 2011 I 2 IRampDM WS1112 D5 Databases amp Information
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Information Retrieval & Data Mining: Transcript
Universität des Saarlandes Saarbrücken Winter Semester 201112 The Course Lecturers Teaching Assistants October 18 2011 I 2 IRampDM WS1112 D5 Databases amp Information Systems Group . CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. naimish.vadodariya@darshan.ac.in. 91-8866215253. Computer Engineering . . Darshan . Institute of Engineering & . Technology. UNIT - 8. Advance Topics. Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . Rafal Lukawiecki. Strategic Consultant, Project Botticelli Ltd. rafal@projectbotticelli.co.uk. Objectives. Overview Data Mining. Introduce typical applications and scenarios. Explain some DM concepts. Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . Virginia Polytechnic Institute and State University. Blacksburg, VA. Professor: E. Fox. . Presenters:. Hossameldin Shahin. Matthew Bock. Michael Cantrell. . May 3rd, 2016. . Agenda. Background. Problem Statement. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Web Mining. Today. Overview of Web Data Mining. Web Content Mining / Text Mining. Web Usage Mining. Web Personalization. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Module 1. Doug . Oard. Segments. The . n. ature of . Information Retrieval. (IR). What . IR . systems. do. The . structure. of interactive IR . systems. Some Holistic Definitions of IR. A . problem-oriented. interesting . and . useful. information from Web . content. and . usage . data. What is Web Mining?. Web mining is . a data . mining . technique . to extract knowledge from . web data. . . Web data includes : . Credit: Gaby . Matalon. What is Data Mining?. The. . process . of analyzing data from different perspectives and summarizing it into useful information. It . uncovers patterns . in a large set of data. Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.
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