'Data instances' diaporamas de présentation

Unsupervised Feature Selection for Linked Social Media Data

Unsupervised Feature Selection for Linked Social Media Data. Jiliang Tang and Huan Liu Computer Science and Engineering Arizona State University August 12-16, 2012 KDD2012. Social Media. The expansive use of social media generates massive data in an unprecedented rate

603 views • 36 slides

O RCHESTRA : Rapid, Collaborative Sharing of Dynamic Data

O RCHESTRA : Rapid, Collaborative Sharing of Dynamic Data. Zachary Ives , Nitin Khandelwal, Aneesh Kapur, University of Pennsylvania Murat Cakir, Drexel University 2 nd Conference on Innovative Database Systems Research January 5, 2005.

286 views • 28 slides

A Genetic Algorithm Approach to K -Means Clustering

447 views • 25 slides

Data Understanding, Cleaning, Transforming

Data Understanding, Cleaning, Transforming. Recall the Data Science Process. Data acquisition Data extraction (wrapper, IE) Understand/clean/transform Integration (resolving schema/instance conflicts) Understand/clean/transform (again if necessary) Further pre-processing

250 views • 24 slides

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Training | Edureka

** Python for Data Science: https://www.edureka.co/python ** This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Check out our playlist: http://bit.ly/2taym8X

1.32k views • 20 slides

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Training | Edureka

** Python for Data Science: https://www.edureka.co/python ** This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes: 1. What is KNN Algorithm? 2. Industrial Use case of KNN Algorithm 3. How things are predicted using KNN Algorithm 4. How to choose the value of K? 5. KNN Algorithm Using Python 6. Implementation of KNN Algorithm from scratch Check out our playlist: http://bit.ly/2taym8X

230 views • 20 slides

Learning to Map between Ontologies on the Semantic Web

Learning to Map between Ontologies on the Semantic Web. AnHai Doan, Jayant Madhavan , Pedro Domingos, and Alon Halevy Databases and Data Mining group University of Washington. Semantic Web. Mark-up data on the web using ontologies Enable intelligent information processing over the web

339 views • 19 slides

Oracle Data Mining Ying Zhang

Oracle Data Mining Ying Zhang. Agenda. Data Mining Data Mining Algorithms Oracle DM Demo. Data, Information, Knowledge. Data Items that are the most elementary descriptions of things, events, activities, and transactions Information Organized data that has meaning and value

493 views • 19 slides

Learning to Map between Ontologies on the Semantic Web

Learning to Map between Ontologies on the Semantic Web. AnHai Doan, Jayant Madhavan , Pedro Domingos, and Alon Halevy Databases and Data Mining group University of Washington. Semantic Web. Mark-up data on the web using ontologies Enable intelligent information processing over the web

202 views • 19 slides

Semantic Mappings for Data Mediation

Semantic Mappings for Data Mediation. Jayant Madhavan University of Washington Joint work with AnHai Doan, Pedro Domingos, and Alon Halevy. Find houses with 2 bedrooms priced under 300K. realestate.com. homeseekers.com. homes.com. Charlie comes to town. Data Integration.

298 views • 17 slides

Multifaceted Exploitation of Metadata for Attribute Match Discovery in Information Integration

Multifaceted Exploitation of Metadata for Attribute Match Discovery in Information Integration. David W. Embley David Jackman Li Xu. Background . Problem : Attribute Matching Matching Possibilities (Facets) Attribute Names Data-Value Characteristics Expected Data Values

175 views • 17 slides

Ming Ji Department of Computer Science University of Illinois at Urbana-Champaign

Active Learning for Information N etworks A Variance Minimization Criterion. Ming Ji Department of Computer Science University of Illinois at Urbana-Champaign. Information Networks: the Data. Information networks Abstraction: graphs

260 views • 10 slides

End-to-End Semantics for Sensor Net 2.0 Prashant Doshi Asst. Professor of Computer Science University of Georgia

UGA. End-to-End Semantics for Sensor Net 2.0 Prashant Doshi Asst. Professor of Computer Science University of Georgia. Generic sensor data publication portal e.g. MSR SenseWeb. Personal sensor data publication portal. Sensor Mashups . Semantic Querying and Data Fusion.

189 views • 3 slides