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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition by Michael J. A. Berry, Gordon S. Linoff

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Index

A

A/B tests

accounts

capturing patterns of

transactions linked to

acquisition, customer

acquisition-time variables

actionable association rules

activation, customer

activation function (artificial neurons)

acyclic graphs

ad hoc text mining (case study)

additive facts (OLAP)

adjusted error rates

advertising

choosing best vehicles for

selecting where to advertise (example)

affinity groups

agent-based modeling

ages of customers, estimating

agglomerative (hierarchical) clustering

algorithm

clustering direct marketing campaigns by customer response

clustering people by age (example)

clustering products by customer preferences

combining nearby clusters

combining with k-means clustering

limitations of

overview

scoring agglomerative clusters

visualizing clusters

aggregating

rows in relational databases

time-stamped transactions

airline complaints (case study)

alleles (genes)

Americans, clustering by demographics

amino acids

analysis of variance (ANOVA)

analysts (data warehousing)

analytic sandboxes

advantage of

defined

purposes of

technologies to support

The Anatomy of a Large-Scale Hypertextual Web Search Engine

anchor point distance (Shazam study)

anonymous transactions

ant world optimization problem (example)

ants, real and artificial

APL programming language

applications

for customer acquisition

for customer relationship masnagement

developers, data warehousing and

architecture for data warehousing

analysts

application developers

business users

central repository

data marts ...

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