'Concept hierarchy' diaporamas de présentation

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Cognition Domain

Cognition Domain. Thinking. Module 24. Module Overview. Concepts Problem Solving Problems Solving Problems. Click on the any of the above hyperlinks to go to that section in the presentation. Concepts. Module 24: Thinking. Cognitive Abilities.

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Instructor: Dan Hebert

Data Warehousing/Mining Comp 150 DW Chapter 4: Data Mining Primitives, Languages, and System Architectures. Instructor: Dan Hebert. Chapter 4: Data Mining Primitives, Languages, and System Architectures. Data mining primitives: What defines a data mining task? A data mining query language

570 views • 41 slides

Instructor: Dan Hebert

Data Warehousing/Mining Comp 150 DW Chapter 4: Data Mining Primitives, Languages, and System Architectures. Instructor: Dan Hebert. Chapter 4: Data Mining Primitives, Languages, and System Architectures. Data mining primitives: What defines a data mining task? A data mining query language

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Text & Web Mining

Text & Web Mining. Structured Data. So far we have focused on mining from structured data:. Attribute ? Value Attribute ? Value Attribute ? Value ? Attribute ? Value. Outlook ? Sunny Temperature ? Hot Windy ? Yes Humidity ? High Play ? Yes. Most data mining involves such data.

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Harshad Kamat SB # 102854314

Harshad Kamat SB # 102854314. CSE 634 - Data Mining Chapter 4 Data Mining Primitives, Languages, and System Architectures. Introduction. Popular Misconception about Data Mining Systems can autonomously dig out all valuable knowledge without human intervention

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Harshad Kamat SB # 102854314

Harshad Kamat SB # 102854314. CSE 634 - Data Mining Chapter 4 Data Mining Primitives, Languages, and System Architectures. Introduction. Popular Misconception about Data Mining Systems can autonomously dig out all valuable knowledge without human intervention

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Data Mining

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Data Mining

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EECS 800 Research Seminar Mining Biological Data

EECS 800 Research Seminar Mining Biological Data. Instructor: Luke Huan Fall, 2006. Administrative. Register for 3 hours of credit. Me. Luke Huan, assistant prof. in Electrical Engineering & Computer Science Homepage: http://people.eecs.ku.edu/~jhuan/ Office: 2304 Eaton Hall

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Event-Based Fusion of Distributed Multimedia Data Sources

Event-Based Fusion of Distributed Multimedia Data Sources. Vincent Oria Department of Computer Science New Jersey Institute of Technology Newark, NJ 07102. Outline. Classical Data Integration Problem Multimedia Data An Architectural approach to Multimedia Data Integration

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Intro to the C++ Standard Template Library (STL)

Intro to the C++ Standard Template Library (STL). The STL is a collection of related software elements Containers Data structures: store values according to a specific organization Iterators Variables used to give flexible access to the values in a container Algorithms

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Adaptive Authoring of Adaptive Educational Hypermedia

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What Defines a Data Mining Task ?

What Defines a Data Mining Task ?. Task-relevant data Type of knowledge to be mined Background knowledge Pattern interestingness measurements Visualization of discovered patterns. Task-Relevant Data (Mineable View). Database or data warehouse name Database tables or data warehouse cubes

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Data Preprocessing

Data Preprocessing. Lecture 12: Overview of data preprocessing Lecture 13: Descriptive data summarization Lecture 14: Data cleaning Lecture 15: Data integration/transformation and data reduction Lecture 16: Discretization and concept hierarchy generation and summary. Discretization.

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Data Preprocessing

Data Preprocessing. Lecture 12: Overview of data preprocessing Lecture 13: Descriptive data summarization Lecture 14: Data cleaning Lecture 15: Data integration/transformation and data reduction Lecture 16: Discretization and concept hierarchy generation and summary. Discretization.

309 views • 12 slides