This document summarizes a lecture on data mining association rules. It introduces the concept of market baskets and frequent itemsets, and describes the Apriori algorithm for finding frequent itemsets. It discusses improvements like the PCY algorithm and multistage approaches. It also covers high-correlation mining to find rules with high confidence even if rare items have low support, and the use of locality-sensitive hashing to efficiently compare minhash signatures of item columns.
Building Maintainable Android Apps (DroidCon NYC 2014)Kevin Schultz
Slides from DroidCon NYC 2014. Covers some ideas around extracting business logic from the app to enable easier unit testing. Much more detail available on my blog (kevinrschultz.com)
This presentation briefly defines machine learning and its types of algorithms. After that two algorithms are presented. The first is naive bayes classifier for text classification and later k-means for clustering including some strategies to improve results.
Building Maintainable Android Apps (DroidCon NYC 2014)Kevin Schultz
Slides from DroidCon NYC 2014. Covers some ideas around extracting business logic from the app to enable easier unit testing. Much more detail available on my blog (kevinrschultz.com)
This presentation briefly defines machine learning and its types of algorithms. After that two algorithms are presented. The first is naive bayes classifier for text classification and later k-means for clustering including some strategies to improve results.
Chapter 6. Mining Frequent Patterns, Associations and Correlations Basic Conc...Subrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
1. CS 361A (Advanced Data Structures and Algorithms) Lecture 20 (Dec 7, 2005) Data Mining: Association Rules Rajeev Motwani (partially based on notes by Jeff Ullman)
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17. Memory Usage – A-Priori Candidate Items Pass 1 Pass 2 Frequent Items Candidate Pairs M E M O R Y M E M O R Y
18.
19. Memory Usage – PCY Candidate Items Pass 1 Pass 2 M E M O R Y M E M O R Y Hash Table Frequent Items Bitmap Candidate Pairs