Skip to Main Content
Knowledge Discovery from Data Streams
book

Knowledge Discovery from Data Streams

by Joao Gama
May 2010
Intermediate to advanced content levelIntermediate to advanced
255 pages
8h 11m
English
Chapman and Hall/CRC
Content preview from Knowledge Discovery from Data Streams
Chapter 7
Frequent Pattern Mining
Frequent itemset mining is one of the most active research topics in knowledge
discovery from databases. The pioneer work was market basket analysis, espe-
cially the task to mine transactional data describing the shopping behavior of
customers. Since then, a large number of efficient algorithms were developed.
In this chapter we review some of the relevant algorithms and their extensions
from itemsets to item sequences.
7.1 Introduction to Frequent Itemset Mining
Let A = {a
1
, . . . , a
m
} be a set of items. Items may be products, special
equipment items, service options etc. Any subset I A is called an item
set. An item ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Reinventing the Organization for GenAI and LLMs

Reinventing the Organization for GenAI and LLMs

Ethan Mollick

Publisher Resources

ISBN: 9781439826126