We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
    cybersecurity efforts
    How Behavioral Analytics and AI Are Redefining Cybersecurity for Boca Raton Businesses
    14 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: book review “Data Preparation For Data Mining”
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > book review “Data Preparation For Data Mining”
Business IntelligenceData Mining

book review “Data Preparation For Data Mining”

TimManns
TimManns
2 Min Read
book review "Data Preparation For Data Mining"
Photo by Alexas_Fotos on Pixabay (https://pixabay.com/photos/frog-cook-spices-preparation-meal-1650035/)
SHARE

Just before Christmas I bought myself yet another data mining book (i have a few dozen). This one somehow slipped by me for 10 years but I’m glad I finally stumbled upon it. Originally published in 1999, Dorian Pyle wrote “Data Preparation For Data Mining” before Data Mining was less wide spread and ‘Predictive Analytics’ wasn’t the buzz word it is today.

The only few criticisms I could possibly raise are;
1) that everything has a statistical basis.
– For example one technique I use to redistribute heavily skewed data is simple binning by count. I work in telecommunications and the behavioural data is always extremely skewed. Log functions don’t work so I often use SQL to convert variables into 100 percentile bins (where each bin has the same number of rows (customers) in it). That type of insight isn’t in the book, but several statistically based alternatives are. I’m not convinced they would work with extremely skewed data, but they are well explained and useful insights.
2) no mention of SQL or step-by-step examples of data manipulation (nothing like ‘before and ‘after’ pictures). Ideas or examples for derived variables are lacking too.

So far I’ve read through the first 275 pages and the odd additional chapter. Its surprisingly easy to read and explains the statistics well. Its definately a book I will refer to, and well worth buying.

– Tim

More Read

8 data mining social networks with more than 2,000 members
8 data mining social networks with more than 2,000 members
Are BI Appliances Simply 30 Year Old Databases?
Mark Drapeau is the Epitome of Government 2.0
LinkedIn mines data for future job paths
How Real-Time Financial Data Makes Small Companies More Robust
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Illustration of mobile analytics dashboards with ad performance charts connected to backend databases
11 Best Sisense Alternatives for Embedded Analytics
Business Intelligence Exclusive
Analyst points at colorful circular data dashboard on screen - information technology business metrics
How Fragmented Workplace Tech Undermines Reliable Business Metrics and Reporting
Cloud Computing Exclusive Infographic IT
Using Multi-Source Data and Analytics to Detect Operational Drift Across Franchise Networks -- AI-generated illustration
Using Multi-Source Data and Analytics to Detect Operational Drift Across Franchise Networks
Exclusive Infographic
Beyond The First Impression: The Long-Lasting Impact Of Sensory Marketing -- AI-generated illustration
Beyond The First Impression: The Long-Lasting Impact Of Sensory Marketing
Infographic Marketing

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Old School: Companies That Still Just Don’t Get It
Business IntelligenceCRM

Old School: Companies That Still Just Don’t Get It

5 Min Read
A Revised "Promised Land" of BI
AnalyticsBusiness Intelligence

A Revised “Promised Land” of BI

4 Min Read
Some of our top Business Intelligence tweets
Business Intelligence

Some of our top Business Intelligence tweets

2 Min Read
The Secret BI / Big Data Playbook
AnalyticsBest PracticesBig DataBusiness IntelligenceCloud ComputingCulture/LeadershipData ManagementMarket Research

The Secret BI / Big Data Playbook

7 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?