Research And Trends In Data Mining Technologies And Applications

Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. Research and Trends in Data Mining Technologies and Applications focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers’ toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.

TABLE OF CONTENT:
Chapter 01 - Combining Data Warehousing and Data Mining Techniques for Web Log Analysis
Chapter 02 - Computing Dense Cubes Embedded in Sparse Data
Chapter 03 - Exploring Similarities Across High-Dimensional Datasets
Chapter 04 - Pattern Comparison in Data Mining: A Survey
Chapter 05 - Mining Frequent Patterns Using Self-Organizing Map
Chapter 06 - An Efficient Compression Technique for Vertical Mining Methods
Chapter 07 - A Tutorial on Hierachical Classification with Applications in Bioinformatics
Chapter 08 - Topological Analysis and Sub-Network Mining of Protein-Protein Interactions
Chapter 09 - Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications
Chapter 10 - Linguistic Rule Extraction from Support Vector Machine Classifiers
Chapter 11 - Graph-Based Data Mining
Chapter 12 - Facilitating and Improving the Use of Web Services with Data Mining

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