Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms

by Jean-Marc Adamo

Overview

Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.

Product Details

ISBN-13:	9781461265115
Publisher:	Springer New York
Publication date:	09/14/2012
Edition description:	Softcover reprint of the original 1st ed. 2001
Pages:	254
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1. Introduction.- 2. Search Space Partition-Based Rule Mining.- 2.1 Problem Statement.- 2.1.1 Canonical Attribute Sequences (cas).- 2.1.2 Database.- 2.1.3 Support.- 2.1.4 Association Rule.- 2.1.5 Problem Statement.- 2.2 Search Space.- 2.3 Splitting Procedure.- 2.4 Enumerating—-Frequent Attribute Sets (cass).- 2.5 Sequential Enumeration Procedure.- 2.6 Parallel Enumeration Procedure.- 2.6.1 Initial Load Balancing.- 2.6.2 Computing the Starting Sets.- 2.6.3 Enumeration Procedure.- 2.6.4 Dynamic Load Balancing.- 2.7 Generating the Association Rules.- 2.7.1 Sequential Generation.- 2.7.2 Parallel Generation.- 3. Apriori and Other Algorithms.- 3.1 Early Algorithms.- 3.1.1 AIS.- 3.1.2 SETM.- 3.2 The Apriori Algorithms.- 3.2.1 Apriori.- 3.2.2 AprioriTid.- 3.3 Direct Hashing and Pruning.- 3.3.1 Filtering Candidates.- 3.3.2 Database Trimming.- 3.3.3 The DHP Algorithm.- 3.4 Dynamic Set Counting.- 4. Mining for Rules over Attribute Taxonomies.- 4.1 Association Rules over Taxonomies.- 4.2 Problem Statement and Algorithms.- 4.3 Pruning Uninteresting Rules.- 4.3.1 Measure of Interest.- 4.3.2 Rule Pruning Algorithm.- 4.3.3 Attribute Presence-Based Pruning.- 5. Constraint-Based Rule Mining.- 5.1 Boolean Constraints.- 5.1.1 Syntax.- 5.1.2 Semantics.- 5.1.3 Propagation of Boolean Constraints.- 5.2 Prime Implicants.- 5.3 Problem Statement and Algorithms.- 6. Data Partition-Based Rule Mining.- 6.1 Data Partitioning.- 6.1.1 Building a Probabilistic Model.- 6.1.2 Bounding Large Deviations for One cas (Chernoff bounds).- 6.1.3 Bounding Large Deviations for Sets of cass.- 6.2 cas Enumeration with Partitioned Data.- 6.2.1 Data Partitioning.- 6.2.2 Local—-Frequent cas Generation.- 6.2.3 Global—-Frequent cas Generation.- 7. Mining for Rules with Categorical and Metric Attributes.- 7.1 Interval Systems and Quantitative Rules.- 7.2 k-Partial Completeness.- 7.3 Pruning Uninteresting Rules.- 7.3.1 Measure of Interest.- 7.3.2 Attribute Presence-Based Pruning.- 7.4 Enumeration Algorithms.- 8. Optimizing Rules with Quantitative Attributes.- 8.1 Solving 1-1-Type Rule Optimization Problems.- 8.1.1 Problem Statement.- 8.1.2 MC\S Problem.- 8.1.3 MS\C Problem.- 8.1.4 MG Problem.- 8.2 Solving d-1-Type Rule Optimization Problems.- 8.3 Solving 1-q-Type Rule Optimization Problems.- 8.3.1 Problem Statement.- 8.3.2 MS\C Problem.- 8.3.3 MG Problem.- 8.4 Solving d-q-Type Rule Optimization Problems.- 8.4.1 Problem Statement.- 8.4.2 Basic Enumeration.- 8.4.3 Enumeration with Pruning.- 8.4.4 Pruning the Instantiation Set.- 9. Beyond Support-Confidence Framework.- 9.1 A Criticism of the Support-Confidence Framework.- 9.2 Conviction.- 9.3 Pruning Conviction-Based Rules.- 9.3.1 Analyzing Conviction.- 9.3.2 Transitivity-Based Pruning.- 9.3.3 Improvement-Based Pruning.- 9.4 One-Step Association Rule Mining.- 9.4.1 Building a Procedure for One-Step Mining.- 9.4.2 Building a Procedure for Improvement-Based Pruning.- 9.5 Correlated Attribute-Set Mining.- 9.5.1 Collective Strength.- 9.5.2 Correlated Attribute-Set Enumeration.- 9.6 Refining Conviction: Association Rule Intensity.- 9.6.1 Measure Construction.- 9.6.2 Properties.- 9.6.3 Relating—-int(s— u) to conv(s— u).- 9.6.4 Mining with the Intensity Measure.- 9.6.5—-Intensity Versus Intensity as Defined in [G96].- 10. Search Space Partition-Based Sequential Pattern Mining.- 10.1 Problem Statement.- 10.1.1 Sequences of cass.- 10.1.2 Database.- 10.1.3 Support.- 10.1.4 Problem Statement.- 10.2 Search Space.- 10.3 Splitting the Search Space.- 10.4 Splitting Procedure.- 10.5 Sequence Enumeration.- 10.5.1 Extending the Support Set Notion.- 10.5.2 Join Operations.- 10.5.3 Sequential Enumeration Procedure.- 10.5.4 Parallel Enumeration Procedure.- Appendix 1. Chernoff Bounds.- Appendix 2. Partitioning in Figure 10.5: Beyond 3rd Power.- Appendix 3. Partitioning in Figure 10.6: Beyond 3rd Power.- References.