Strategic Advancements in Utilizing Data Mining and Warehousing Technologies: New Concepts and Developments, 1st Edition

  • Published By:
  • ISBN-10: 1605667188
  • ISBN-13: 9781605667188
  • DDC: 005.74
  • Grade Level Range: College Freshman - College Senior
  • 342 Pages | eBook
  • Original Copyright 2009 | Published/Released June 2010
  • This publication's content originally published in print form: 2009

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About

Overview

Organizations rely on data mining and warehousing technologies to store, integrate, query, and analyze essential data. Strategic Advancements in Utilizing Data Mining and Warehousing Technologies: New Concepts and Developments discusses developments in data mining and warehousing as well as techniques for successful implementation. Contributions investigate theoretical queries along with real-world applications, providing a useful foundation for academicians and practitioners to research new techniques and methodologies.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Advisory Board.
Table of Contents.
Detailed Table of Contents.
Preface.
1: A Methodology Supporting the Design and Evaluating the Final Quality of Data Warehouses.
2: Seismological Data Warehousing and Mining: A Survey.
3: Automated Integration of Heterogeneous Data Warehouse Schemas.
4: Algebraic and Graphic Languages for OLAP Manipulations.
5: Dynamic View Selection for OLAP.
6: RCUBE: Parallel Multi-Dimensional ROLAP Indexing.
7: Medical Document Clustering Using Ontology-Based Term Similarity Measures.
8: A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information.
9: An Integrated Framework for Fuzzy Classification and Analysis of Gene Expression Data.
10: Vertical Fragmentation in Databases Using Data-Mining Technique.
11: Introducing the Elasticity of Spatial Data.
12: Sequential Patterns Postprocessing for Structural Relation Patterns Mining.
13: MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns.
14: Computing Join Aggregates Over Private Tables.
15: Overview of PAKDD Competition 2007.
16: A Solution to the Cross-Selling Problem of PAKDD-2007: Ensemble Model of TreeNet and Logistic Regression.
17: Bagging Probit Models for Unbalanced Classification.
18: The Power of Sampling and Stacking for the PAKDD-2007 Cross-Selling Problem.
19: Using TreeNet to Cross-Sell Home Loans to Credit Card Holders.
20: PAKDD-2007: A Near-Linear Model for the Cross-Selling Problem.
21: Selecting Salient Features and Samples Simultaneously to Enhance Cross-Selling Model Performance.
22: Classification of Imbalanced Data with Random Sets and Mean-Variance Filtering.
23: Ranking Potential Customers Based on Group-Ensemble.
Compilation of References.
About the Contributors.
Index.