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Reconstruction of regulatory modules based on heterogeneous data sources

Reconstruction of regulatory modules based on heterogeneous data sources

Reconstruction of regulatory modules based on heterogeneous data sources. Karen Lemmens PhD Defence September 29th 2008. Outline. 1. Introduction 2. Strategy 3. Achievements 4. Conclusions. Introduction & objectives Strategy Data integration

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758 views • 65 slides
Extending DSMS for Data Stream Mining

Extending DSMS for Data Stream Mining

Extending DSMS for Data Stream Mining. CS240B Notes by Carlo Zaniolo UCLA CSD. Data Streams. Continuous, unbounded, rapid, time-varying streams of data elements Occur in a variety of modern applications Network monitoring and traffic engineering Sensor networks, RFID tags

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523 views • 37 slides
Extending DSMS for Data Stream Mining

Extending DSMS for Data Stream Mining

Extending DSMS for Data Stream Mining. CS240B Notes by Carlo Zaniolo UCLA CSD. Data Streams. Continuous, unbounded, rapid, time-varying streams of data elements Occur in a variety of modern applications Network monitoring and traffic engineering Sensor networks, RFID tags

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401 views • 37 slides
An Overview of Domain-Driven Data Mining: Toward Actionable Knowledge Discovery (AKD)

An Overview of Domain-Driven Data Mining: Toward Actionable Knowledge Discovery (AKD)

An Overview of Domain-Driven Data Mining: Toward Actionable Knowledge Discovery (AKD). Longbing Cao Faculty of Engineering and Information Technology University of Technology, Sydney, Australia. Outline. Why Do We Need D 3 M What Is D 3 M The D 3 M Framework

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699 views • 36 slides
Mining Positive and Negative Patterns for Relevance Feature Discovery

Mining Positive and Negative Patterns for Relevance Feature Discovery

Mining Positive and Negative Patterns for Relevance Feature Discovery. Presenter : Cheng- Hui Chen Author : Yuefeng Li, Abdulmohsen Algarni , Ning Zhong KDD 2010. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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385 views • 20 slides
Toward Knowledge Discovery in Databases Attached to Grids

Toward Knowledge Discovery in Databases Attached to Grids

Toward Knowledge Discovery in Databases Attached to Grids. Peter Brezany Insti tute for Software Science Univers ity of Vienna E-mail : brezany@par.univie.ac.at. Media That Radically Influenced Society. 1850s Telegraph. 1840s Penny Post. 1500s Printing Press. 1930s

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273 views • 16 slides
6. Spatial Mining

6. Spatial Mining

6. Spatial Mining. Spatial Data and Structures Images Spatial Mining Algorithms. Definitions. Spatial data is about instances located in a physical space Spatial data has location or geo-referenced features Some of these features are: Address, latitude/longitude (explicit)

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333 views • 16 slides
CS 401 Paper Presentation Praveen Inuganti

CS 401 Paper Presentation Praveen Inuganti

Data Preparation for Mining World Wide Web Browsing Patterns Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava. CS 401 Paper Presentation Praveen Inuganti. Overview. Data cleaning User identification Session identification Path completion. Introduction

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234 views • 15 slides
X11 Algorithm Development Company

X11 Algorithm Development Company

Developcoins is a predominant X11 Algorithm Development Company offering exclusive cryptocurrency Creation Services to build cryptocurrency will premium quality and features like Bitcoin. Being skilled at cryptocurrency and easily adapt to emerging technologies, we also develop customizable crypto coin, as per your requirements in the name you desire. https://www.developcoins.com/x11-algorithm-development

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153 views • 14 slides
System Support for High Performance Scientific Data Mining

System Support for High Performance Scientific Data Mining

System Support for High Performance Scientific Data Mining. Gagan Agrawal Ruoming Jin Raghu Machiraju S. Parthasarathy Department of Computer and Information Sciences Ohio State University. Scientific Data Mining Problem.

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270 views • 12 slides
System Support for High Performance Scientific Data Mining

System Support for High Performance Scientific Data Mining

System Support for High Performance Scientific Data Mining. Gagan Agrawal Ruoming Jin Raghu Machiraju S. Parthasarathy Department of Computer and Information Sciences Ohio State University. Scientific Data Mining Problem.

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131 views • 12 slides

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