LNAI 2682 Condensed Representations for Sets of Mining Queries 1st Edition by Arnaud Giacometti, Dominique Laurent, Cheikh Talibouya Diop ISBN 9783540224792 354022479X

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Product details:
ISBN 10: 354022479X
ISBN 13: 9783540224792
Author: Arnaud Giacometti, Dominique Laurent, Cheikh Talibouya Diop

In this paper, we propose a general framework for condensed representations of sets of mining queries. To this end, we adapt the standard notions of maximal, closed and key patterns introduced in previous works, including those dealing with condensed representations. Whereas these previous works concentrate on condensed representations of the answer to a single mining query, we consider the more general case of sets of mining queries defined by monotonic and anti-monotonic selection predicates.

LNAI 2682 Condensed Representations for Sets of Mining Queries 1st Edition Table of contents:

Chapter 1: Introduction to Data Mining Queries

• What Are Mining Queries?
• Types of Data Mining Queries: Classification, Clustering, Association
• Challenges in Efficient Query Representation
• The Importance of Condensed Representations in Data Mining

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Chapter 2: Fundamentals of Set Representation in Data Mining

• The Concept of Sets in Data Mining
• Representation of Large Data Sets and Queries
• Efficient Storage and Retrieval of Mining Queries
• The Role of Condensed Representations in Reducing Complexity

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Chapter 3: Condensed Representations for Mining Queries

• Defining Condensed Representations
• Principles of Query Minimization
• Techniques for Condensing Mining Queries
• Use Cases and Benefits of Condensed Representations

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Chapter 4: Algorithms for Condensed Representations

• Overview of Algorithms for Query Reduction
• Greedy Algorithms for Set Compression
• Approximation Algorithms for Large-Scale Queries
• Performance Evaluation and Trade-Offs

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Chapter 5: Condensing Association Rule Mining Queries

• Association Rule Mining and Its Challenges
• Techniques for Condensing Association Rules
• Reducing Redundancy in Rule-Based Queries
• Case Studies of Condensed Association Queries

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Chapter 6: Condensed Representations for Classification Queries

• Classification Algorithms and Query Reduction
• Techniques for Reducing Decision Trees and Rules
• Condensing Support Vector Machines (SVM) Queries
• Evaluation of Classification Query Representations

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Chapter 7: Condensed Representations for Clustering Queries

• Overview of Clustering Algorithms
• Reducing Clustering Query Complexity
• Representing Centroids and Cluster Memberships
• Applications of Condensed Representations in Clustering

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Chapter 8: Implementing Condensed Representations

• Tools and Techniques for Implementation
• Using Condensed Representations in Real-World Systems
• Integrating Condensed Representations with Data Mining Frameworks
• Performance Analysis and Optimization

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Chapter 9: Applications of Condensed Mining Queries

• Applications in Large-Scale Data Mining
• Real-World Case Studies: E-Commerce, Healthcare, and Finance
• Efficient Querying for Big Data Analytics
• Future Directions for Condensed Representations

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Chapter 10: Future Trends in Data Mining Query Representations

• Advances in Query Representation Techniques
• Integrating Machine Learning with Data Mining Queries
• Real-Time Querying and Optimization
• Challenges and Open Research Areas

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