國立虎尾科技大學 |

Language: English

Back

IBM SPSS Modeler cookbook : = over 6...

Shearer, Colin.

IBM SPSS Modeler cookbook : = over 60 practical recipes to achieve better results using the experts' methods for data mining /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	IBM SPSS Modeler cookbook :/ Keith McCormick ... [et al.] ; foreword by Colin Shearer.
Reminder of title:	over 60 practical recipes to achieve better results using the experts' methods for data mining /
remainder title:	SPSS Modeler cookbook
other author:	Shearer, Colin.
Published:	Birmingham, UK :Packt Pub., : 2013.,
Description:	iii, 359 p. :ill. ; : 24 cm.;
Notes:	Includes index.
Subject:	Data mining. -
ISBN:	9781849685467 (Pbk) :

IBM SPSS Modeler cookbook : = over 60 practical recipes to achieve better results using the experts' methods for data mining /
IBM SPSS Modeler cookbook :over 60 practical recipes to achieve better results using the experts' methods for data mining /SPSS Modeler cookbookKeith McCormick ... [et al.] ; foreword by Colin Shearer. - Birmingham, UK :Packt Pub.,2013. - iii, 359 p. :ill. ;24 cm.

Includes index.

Cover; Copyright; Credits; Foreword; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Understanding; Introduction; Using an empty aggregate to evaluate sample size; Evaluating the need to sample from the initial data; Using CHAID stumps when interviewing an SME; Using a single cluster K-means as an alternative to anomaly detection; Using an @NULL multiple Derive to explore missing data; Creating an outlier report to give to SMEs; Detecting potential model instability early using the Partition node and Feature Selection.

ISBN: 9781849685467 (Pbk) :NT1881

Nat. Bib. No.: GBB747746bnbSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343 / I35 2013

Dewey Class. No.: 006.312

IBM SPSS Modeler cookbook : = over 60 practical recipes to achieve better results using the experts' methods for data mining /
LDR:03957cam a2200289 a 4500 001 959397
008 201127s2013 enka 001 0 eng
015 $a GBB747746 $2 bnb
020 $a 9781849685467 (Pbk) : $c NT1881
020 $a 1849685460 (Pbk)
020 $a 9781849685474 (ebook)
020 $a 1849685479 (ebook)
035 $a 020672333
040 $a UMI $b eng $e pn $c UMI $d COO $d DEBBG $d DEBSZ $d OCLCF $d EBLCP $d IDEBK $d YDXCP $d S4S $d E7B $d OCLCQ $d Uk $c NFU
050 # 4 $a QA76.9.D343 $b I35 2013
082 0 4 $a 006.312 $2 23
245 0 0 $a IBM SPSS Modeler cookbook : $b over 60 practical recipes to achieve better results using the experts' methods for data mining / $c Keith McCormick ... [et al.] ; foreword by Colin Shearer.
246 3 0 $a SPSS Modeler cookbook
246 3 0 $a Over 60 practical recipes to achieve better results using the experts' methods for data mining
260 # $a Birmingham, UK : $b Packt Pub., $c 2013.
300 $a iii, 359 p. : $b ill. ; $c 24 cm.
500 $a Includes index.
500 $a Academic.
505 0 # $a Cover; Copyright; Credits; Foreword; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Data Understanding; Introduction; Using an empty aggregate to evaluate sample size; Evaluating the need to sample from the initial data; Using CHAID stumps when interviewing an SME; Using a single cluster K-means as an alternative to anomaly detection; Using an @NULL multiple Derive to explore missing data; Creating an outlier report to give to SMEs; Detecting potential model instability early using the Partition node and Feature Selection.
505 8 # $a Chapter 2: Data Preparation -- SelectIntroduction; Using the Feature Selection node creatively to remove, or decapitate, perfect predictors; Running a Statistics node on anti-join to evaluate potential missing data; Evaluating the use of sampling for speed; Removing redundant variables using correlation matrices; Selecting variable using the CHAID modeling node; Selecting variables using the Means node; Selecting variables using single-antecedent association rules; Chapter 3: Data Preparation -- Clean; Introduction; Binning scale variables to address missing data.
505 8 # $a Using a full data model/partial data model approach to address missing dataImputing in-stream mean or median; Imputing missing values randomly from uniform or normal distributions; Using random imputation to match a variable's distribution; Searching for similar records using a neural network for inexact matching; Using neuro-fuzzy searching to find similar names; Producing longer Soundex codes; Chapter 4: Data Preparation -- Construct; Introduction; Building transformations with multiple Derive nodes; Calculating and comparing conversion rates; Grouping categorical values.
505 8 # $a Transforming high skew and kurtosis variables with a multiple Derive nodeCreating flag variables for aggregation; Using Association Rules for interaction detection/feature creation; Creating time-aligned cohorts; Chapter 5: Data Preparation -- Integrate and Format; Introduction; Speeding up merge with caching and optimization settings; Merging a look-up table; Shuffle-down (nonstandard aggregation); Cartesian product merge using key-less merge by key; Multiplying out using Cartesian product merge, user source, and derive dummy; Changing large numbers of variable names without scripting.
505 8 # $a Parsing nonstandard datesParsing and performing a conversion on a complex stream; Sequence processing; Chapter 6: Selecting and Building a Model; Introduction; Evaluating balancing with the Auto Classifier; Building models with and without outliers; Neural Network Feature Selection; Creating a bootstrap sample; Creating bagged logistic regression models; Using KNN to match similar cases; Using Auto Classifier to tune models; Next-Best-Offer for large datasets; Chapter 7: Modeling -- Assessment, Evaluation, Deployment, and Monitoring; Introduction; How (and why) to validate as well as test.
650 # 0 $a Data mining. $3 528622
700 1 # $a Shearer, Colin. $3 1252496
700 1 # $a McCormick, Keith. $3 1177567