Overview

Data mining includes a wide range of activities such as classification, clustering, similarity analysis, summarization, association rule and sequential pattern discovery, and so forth. The book focuses on the last two previously listed activities. It provides a unified presentation of algorithms for association rule and sequential pattern discovery. For both mining problems, the presentation relies on the lattice structure of the search space. All algorithms are built as processes running on this structure. Proving their properties takes advantage of the mathematical properties of the structure. Part of the motivation for writing this book was postgraduate teaching. One of the main intentions was to make the book a suitable support for the clear exposition of problems and algorithms as well as a sound base for further discussion and investigation. Since the book only assumes elementary mathematical knowledge in the domains of lattices, combinatorial optimization, probability calculus, and statistics, it is fit for use by undergraduate students as well. The algorithms are described in a C-like pseudo programming language. The computations are shown in great detail. This makes the book also fit for use by implementers: computer scientists in many domains as well as industry engineers.

Full Product Details

Author:   Jean-Marc Adamo
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2001
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   0.415kg
ISBN:  

9781461265115

ISBN 10:   1461265118
Pages:   254
Publication Date:   14 September 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. Introduction.- 2. Search Space Partition-Based Rule Mining.- 2.1 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.- 2.7 Generating the Association Rules.- 3. Apriori and Other Algorithms.- 3.1 Early Algorithms.- 3.2 The Apriori Algorithms.- 3.3 Direct Hashing and Pruning.- 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.- 5. Constraint-Based Rule Mining.- 5.1 Boolean Constraints.- 5.2 Prime Implicants.- 5.3 Problem Statement and Algorithms.- 6. Data Partition-Based Rule Mining.- 6.1 Data Partitioning.- 6.2 cas Enumeration with Partitioned Data.- 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.- 7.4 Enumeration Algorithms.- 8. Optimizing Rules with Quantitative Attributes.- 8.1 Solving 1-1-Type Rule Optimization Problems.- 8.2 Solving d-1-Type Rule Optimization Problems.- 8.3 Solving 1-q-Type Rule Optimization Problems.- 8.4 Solving d-q-Type Rule Optimization Problems.- 9. Beyond Support-Confidence Framework.- 9.1 A Criticism of the Support-Confidence Framework.- 9.2 Conviction.- 9.3 Pruning Conviction-Based Rules.- 9.4 One-Step Association Rule Mining.- 9.6 Refining Conviction: Association Rule Intensity.- 10. Search Space Partition-Based Sequential Pattern Mining.- 10.1 Problem Statement.- 10.2 Search Space.- 10.3 Splitting the Search Space.- 10.4 Splitting Procedure.- 10.5 Sequence Enumeration.- Appendix 1. Chernoff Bounds.- Appendix 2. Partitioning in Figure 10.5: Beyond3rd Power.- Appendix 3. Partitioning in Figure 10.6: Beyond 3rd Power.- References.

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