PPT-Web Mining

Shah Mohammad Nur Alam Sawn 03032014 What is Web Mining Discovering desired and useful information from the World Wide Web Exploiting Geographical Location Information of Web Pages. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Sam Chikowore. – Exporien Mining. Zimbabwe Mining and Infrastructure Indaba . 2013.. WHO ARE THEY?. THE ARTISANAL MINERS. THE MINING CO-OPERATIVES. WOMEN MINING CO-OPERATIVES. THE JUNIOR MINING COMPANIES. PRESENTATION BY .  . THE SECRETARY FOR MINES AND MINING DEVELOPMENT .  . PROF. F. P. GUDYANGA.  . 7. th. ZIMBABWE MINING & INFRASTRUCTURE INDABA. . 2015. CONTENTS. INTRODUCTION. MINERALS AND MATERIALS. Oman. Hilal al-. Busaidi. Public Authority for Mining. Outlines. Mining History in Oman. Presence of Minerals . in Oman. Mining . Sector in Om. an. Mining . Investment . Opportunities. . Mining History in Oman. (Part 1). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . 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. Feel free to use these slides verbatim, or to modify them to fit your own needs. (Part . 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . 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. Feel free to use these slides verbatim, or to modify them to fit your own needs. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . 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. Feel free to use these slides verbatim, or to modify them to fit your own needs. 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. Frequent Itemset Mining & Association Rules Mining of Massive Datasets Jure Leskovec, Anand Rajaraman , Jeff Ullman Stanford University http://www.mmds.org Note to other teachers and users of these http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am. Cindi Godsey, Permit Writer and Alaska Mining Coordinator, US EPA Region 10. Patty McGrath, Permitting Manager, . Donlin. Gold LLC. Lorraine Edmond. , Hydrogeologist, US EPA Region 10. Mining Information Session. 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 : . 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.