'Attribute selection' presentation slideshows

Optimization Methods in Data Mining

Optimization Methods in Data Mining. Overview. Optimization. Combinatorial Optimization. Mathematical Programming. Support Vector Machines. Genetic Algorithm. Steepest Descent Search. Neural Nets, Bayesian Networks (optimize parameters). Feature selection Classification

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Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8, Data transformations of Data Mining by I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal. Data transformations. Attribute selection: Scheme- independent and scheme -specific

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Data Mining Techniques: Classification

Data Mining Techniques: Classification. Classification. What is Classification? Classifying tuples in a database In training set E each tuple consists of the same set of multiple attributes as the tuples in the large database W additionally, each tuple has a known class identity

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Data Mining with Weka Putting it all together

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CLASSIFICATION BOUNDARIES

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Exploratory Data Mining and Data Preparation

Exploratory Data Mining and Data Preparation. Data evaluation. Data preparation. Modeling. Evaluation. The Data Mining Process. Business understanding. Data. Deployment. Exploratory Data Mining. Preliminary process Data summaries Attribute means Attribute variation

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Knowledge-Based Persistent Archives Reagan W. Moore San Diego Supercomputer Center

Knowledge-Based Persistent Archives Reagan W. Moore San Diego Supercomputer Center 9500 Gilman Drive, La Jolla, CA 92093-0505 Phone: 858 534-5073 FAX: 858 534-5152 E-mail: moore@sdsc.edu. Staff Reagan Moore Chaitan Baru Sheau Yen Chen Charles Cowart Amarnath Gupta George Kremenek

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Data Engineering Data preprocessing and transformation

Data Engineering Data preprocessing and transformation. Just apply a learner? No!. Algorithms are biased No free lunch theorem: considering all possible data distributions, no algorithm is better than another Algorithms make assumptions about data

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Contributions to MiningMart

Contributions to MiningMart. Petr Berka Laboratory for Intelligent Systems University of Economics, Prague berka@vse.cz. University of Economics, Prague. LISp - Laboratory for Intelligent Systems

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Contributions to MiningMart

Contributions to MiningMart. Petr Berka Laboratory for Intelligent Systems University of Economics, Prague berka@vse.cz. University of Economics, Prague. LISp - Laboratory for Intelligent Systems

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Evaluation of WEKA

Evaluation of WEKA. Waikato Environment for Knowledge Analysis Presented By: Manoj Wartikar & Sameer Sagade. Outline. Introduction to the WEKA System. Features Pros and Cons Enhancements. Introduction. A research project at the University of Waikato, NZ

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Toward Entity Retrieval over Structured and Text Data

Toward Entity Retrieval over Structured and Text Data. Mayssam Sayyadian, Azadeh Shakery, AnHai Doan, ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Presentation at ACM SIGIR 2004 Workshop on Information Retrieval and Databases, July 29, 2004.

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Machine Learning and Bioinformatics ??????????

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Reducing the Response Time for Data Warehouse Queries Using Rough Set Theory

Reducing the Response Time for Data Warehouse Queries Using Rough Set Theory. By Mahmoud Mohamed Al-Bouraie Yasser Fouad Mahmoud Hassan Wesam Fathy Jasser. Outline. Aim Pre-grouping Transformation Hierarchical pre-grouping Attribute Selection A Heuristic Algorithm for Attribute Selection

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Machine Learning in Football

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Photo Red Light (PRL) Placement in the City of Covina

Photo Red Light (PRL) Placement in the City of Covina. By Paola Bassignana. Introduction: The Covina PD PRL Assessment . What is it? A report that: Assesses success of 3 PRLs in the city Provides statistics for accidents in 46 major intersections

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