'Numeric attributes' presentation slideshows

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 6, Trees and Rules of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Algorithms for learning trees and rules. Decision trees From ID3 to C4.5 (pruning, numeric attributes, ...)

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Data Mining in Market Research

Data Mining in Market Research. What is data mining? Methods for finding interesting structure in large databases E.g. patterns, prediction rules, unusual cases Focus on efficient, scalable algorithms Contrasts with emphasis on correct inference in statistics

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Exchanges for Complex Commodities: Toward a General-Purpose System

Exchanges for Complex Commodities: Toward a General-Purpose System. John Hershberger. Committee: Eugene Fink Rafael Perez Dmitry B. Goldgof. Combine the speed of the stock exchange. with the flexibility of eBay. +. Motivation. Build an automated

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CLASSIFICATION BOUNDARIES

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Machine Learning in Practice Lecture 7

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Unsupervised Learning with Mixed Numeric and Nominal Data

Unsupervised Learning with Mixed Numeric and Nominal Data. Advisor : Dr. Hsu Graduate : Yu-Cheng Chen Authors : Cen Li, Gautam Biswas. 2002 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. Outline. Motivation Objective Introduction Background SABC

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Algorithms for Classification:

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Algorithms for Classification:

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Data preparation: Selection, Preprocessing, and Transformation

Data preparation: Selection, Preprocessing, and Transformation. Literature: I.H. Witten and E. Frank, Data Mining, chapter 2 and chapter 7. Knowledge. Transformed data. Patterns. Target data. Processed data. Interpretation Evaluation. Data Mining. Transformation & feature

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Data preparation: Selection, Preprocessing, and Transformation

Data preparation: Selection, Preprocessing, and Transformation. Literature: I.H. Witten and E. Frank, Data Mining, chapter 2 and chapter 7. Knowledge. Transformed data. Patterns. Target data. Processed data. Interpretation Evaluation. Data Mining. Transformation & feature

434 views • 25 slides

Chapter 6: Implementations

Chapter 6: Implementations. Why are simple methods not good enough?. Robustness: Numeric attributes, missing values, and noisy data. Decision Trees. Divide and conquer method Earlier discussion of this method worked for nominal values. How to deal with numeric attributes?

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Classification

Classification. Classification. Task: Given a set of pre-classified examples, build a model or classifier to classify new cases. Supervised learning: classes are known for the examples used to build the classifier. A classifier can be a set of rules, a decision tree, a neural network, etc.

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Data Mining CSCI 307, Spring 2019 Lecture 13

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