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 Free database ebooks Other database ebooks Research and Trends in Data Mining Technologies and Applications
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 Section I, on Data Warehousing and Mining, consists of three chapters covering data mining techniques applied to data warehouse Web logs, data cubes, and highdimensional datasets.Chapter.I, “Combining Data Warehousing and Data Mining Techniques for Web Log Analysis” There are several approaches to analyze Web logs. They propose a hybrid method that combines data warehouse Web log schemas and a data mining technique called Hyper Probabilistic Grammars, resulting in a fast and flexible Web log analysis. Chapter.II, “Computing Dense Cubes Embedded in Sparse Data” The chapter proposes a new dynamic data structure called Restricted Sparse Statistics Trees and a cube evaluation algorithm to efficiently compute dense sub-cubes embedded in highdimensional sparse input datasets. Chapter.III, “Exploring Similarities Across High-Dimensional Datasets” Their analysis shows that they could find more interesting results from the combined model than those obtained from independent analysis of the original datasets. Section II, on Patterns, consists of three chapters covering pattern comparisons, frequent patterns, and vertical mining patterns. Chapter.IV, “Pattern Comparison in Data Mining: A Survey”In this chapter, the authors focus on pattern comparison in frequent itemsets and association rules, clusters and clusterings, and decision trees. Chapter.V, “Mining Frequent Patterns Using Self-Organizing Map”This chapter discusses issues of using a SOM clustering technique for the purpose of generating association rules. It also includes some case studies comparing the SOM approach and the traditional association rule approach. Chapter.VI, “An Efficient Compression Technique for Vertical Mining Methods” This chapter proposes an algorithm for vertical association rule mining that compresses a vertical dataset in an efficient manner by utilizing bit vectors. Section III, on Data Mining in Bioinformatics, presents data mining applications in the bioinformatics domain. This part consists of two chapters covering hierarchical classification, and topological analysis and sub-network mining. Chapter.VII, “A Tutorial on Hierarchical Classification with Applications in Bioinformatics” presents a comprehensive tutorial on complex classification problems suitable for bioinformatics applications, particularly the prediction of protein function, whereby the predicted classes are hierarchical in nature. Chapter.VIII, “Topological Analysis and Sub-Network Mining of Protein: Protein Interactions” The authors also report some statistical analysis whereby the results obtained are far from a power law, contradicting many published results. The final section of this volume, Data Mining Techniques, consists of four chapters, covering data mining techniques using multiple criteria optimization, support vector machine classifiers, graph-based mining, and Web services. Chapter.IX, “Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications”The chapter includes some case studies, including credit card scoring, HIV-1, and network introduction detection. Chapter.X, “Linguistic Rule Extraction from Support Vector Machine Classifiers” They show that the rule extraction results from the proposed method could follow SVM classifier decisions very well. Chapter.XI, “Graph-Based Data Mining”This chapter particularly focuses on approaches that are potentially valuable to graph-based data mining. Chapter.XII, “Facilitating and Improving the Use of Web Services with Data Mining”This chapter examines how some of the issues of Web services can be addressed through data mining. Overall, this volume covers important foundations to research and applications in data mining, covering patterns and techniques, as well as issues of mining data warehouses and an important application domain, namely bioinformatics. The different types of chapters, some of which are surveys and tutorials, while others propose novel techniques and algorithms, show a full spectrum of the coverage of this important topic. Download free pdf ebooks on database:Research and Trends in Data Mining Technologies and Applications
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