The Resource Principles of data mining, Max Bramer
Principles of data mining, Max Bramer
Resource Information
The item Principles of data mining, Max Bramer represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Principles of data mining, Max Bramer represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
- Summary
- Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science
- Language
- eng
- Edition
- 2nd ed.
- Extent
- 1 online resource.
- Contents
-
- Avoiding Overfitting of Decision Trees
- More About Entropy
- Inducing Modular Rules for Classification
- Measuring the Performance of a Classifier
- Dealing with Large Volumes of Data
- Ensemble Classification
- Comparing Classifiers
- Association Rule Mining I
- Association Rule Mining II
- Association Rule Mining III: Frequent Pattern Trees
- Introduction to Data Mining
- Clustering
- Text Mining
- Data for Data Mining
- Introduction to Classification: Naïve Bayes and Nearest Neighbour
- Using Decision Trees for Classification
- Decision Tree Induction: Using Entropy for Attribute Selection
- Decision Tree Induction: Using Frequency Tables for Attribute Selection
- Estimating the Predictive Accuracy of a Classifier
- Continuous Attributes
- Isbn
- 9781447148845
- Label
- Principles of data mining
- Title
- Principles of data mining
- Statement of responsibility
- Max Bramer
- Language
- eng
- Summary
- Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science
- Cataloging source
- GW5XE
- http://library.link/vocab/creatorDate
- 1948-
- http://library.link/vocab/creatorName
- Bramer, M. A.
- Dewey number
- 006.312
- Illustrations
- illustrations
- Index
- index present
- LC call number
- QA76.9.D343
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- NLM call number
- QA 76.9.D343
- Series statement
- Undergraduate topics in computer science
- http://library.link/vocab/subjectName
-
- Data mining
- Data Mining
- Data mining
- Label
- Principles of data mining, Max Bramer
- Antecedent source
- unknown
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Avoiding Overfitting of Decision Trees
- More About Entropy
- Inducing Modular Rules for Classification
- Measuring the Performance of a Classifier
- Dealing with Large Volumes of Data
- Ensemble Classification
- Comparing Classifiers
- Association Rule Mining I
- Association Rule Mining II
- Association Rule Mining III: Frequent Pattern Trees
- Introduction to Data Mining
- Clustering
- Text Mining
- Data for Data Mining
- Introduction to Classification: Naïve Bayes and Nearest Neighbour
- Using Decision Trees for Classification
- Decision Tree Induction: Using Entropy for Attribute Selection
- Decision Tree Induction: Using Frequency Tables for Attribute Selection
- Estimating the Predictive Accuracy of a Classifier
- Continuous Attributes
- Control code
- 828676626
- Dimensions
- unknown
- Edition
- 2nd ed.
- Extent
- 1 online resource.
- File format
- unknown
- Form of item
- online
- Isbn
- 9781447148845
- Level of compression
- unknown
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-4471-4884-5
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Sound
- unknown sound
- Specific material designation
- remote
- System control number
- (OCoLC)828676626
- Label
- Principles of data mining, Max Bramer
- Antecedent source
- unknown
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Avoiding Overfitting of Decision Trees
- More About Entropy
- Inducing Modular Rules for Classification
- Measuring the Performance of a Classifier
- Dealing with Large Volumes of Data
- Ensemble Classification
- Comparing Classifiers
- Association Rule Mining I
- Association Rule Mining II
- Association Rule Mining III: Frequent Pattern Trees
- Introduction to Data Mining
- Clustering
- Text Mining
- Data for Data Mining
- Introduction to Classification: Naïve Bayes and Nearest Neighbour
- Using Decision Trees for Classification
- Decision Tree Induction: Using Entropy for Attribute Selection
- Decision Tree Induction: Using Frequency Tables for Attribute Selection
- Estimating the Predictive Accuracy of a Classifier
- Continuous Attributes
- Control code
- 828676626
- Dimensions
- unknown
- Edition
- 2nd ed.
- Extent
- 1 online resource.
- File format
- unknown
- Form of item
- online
- Isbn
- 9781447148845
- Level of compression
- unknown
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-4471-4884-5
- Quality assurance targets
- not applicable
- Reformatting quality
- unknown
- Sound
- unknown sound
- Specific material designation
- remote
- System control number
- (OCoLC)828676626
Subject
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Principles-of-data-mining-Max-Bramer/oLd-cBFUD2I/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Principles-of-data-mining-Max-Bramer/oLd-cBFUD2I/">Principles of data mining, Max Bramer</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
