The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

The concept of a linguistic variable and its application to approximate reasoning—II☆

Data Mining - Concepts and Techniques.

This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity. The authors present the theory in a concise and straightforward manner, with an eye out for the practical applications. Exercises at the end of each chapter, including some that have been solved, help readers confirm and enhance their understanding of the material. This book is appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.

Introduction to Automata Theory, Languages, and Computation

1 Introduction 1.1 Language Processors 1.2 The Structure of a Compiler 1.3 The Evolution of Programming Languages 1.4 The Science of Building a Compiler 1.5 Applications of Compiler Technology 1.6 Programming Language Basics 1.7 Summary of Chapter 1 1.8 References for Chapter 1 2 A Simple Syntax-Directed Translator 2.1 Introduction 2.2 Syntax Definition 2.3 Syntax-Directed Translation 2.4 Parsing 2.5 A Translator for Simple Expressions 2.6 Lexical Analysis 2.7 Symbol Tables 2.8 Intermediate Code Generation 2.9 Summary of Chapter 2 3 Lexical Analysis 3.1 The Role of the Lexical Analyzer 3.2 Input Buffering 3.3 Specification of Tokens 3.4 Recognition of Tokens 3.5 The Lexical-Analyzer Generator Lex 3.6 Finite Automata 3.7 From Regular Expressions to Automata 3.8 Design of a Lexical-Analyzer Generator 3.9 Optimization of DFA-Based Pattern Matchers 3.10 Summary of Chapter 3 3.11 References for Chapter 3 4 Syntax Analysis 4.1 Introduction 4.2 Context-Free Grammars 4.3 Writing a Grammar 4.4 Top-Down Parsing 4.5 Bottom-Up Parsing 4.6 Introduction to LR Parsing: Simple LR 4.7 More Powerful LR Parsers 4.8 Using Ambiguous Grammars 4.9 Parser Generators 4.10 Summary of Chapter 4 4.11 References for Chapter 4 5 Syntax-Directed Translation 5.1 Syntax-Directed Definitions 5.2 Evaluation Orders for SDD's 5.3 Applications of Syntax-Directed Translation 5.4 Syntax-Directed Translation Schemes 5.5 Implementing L-Attributed SDD's 5.6 Summary of Chapter 5 5.7 References for Chapter 5 6 Intermediate-Code Generation 6.1 Variants of Syntax Trees 6.2 Three-Address Code 6.3 Types and Declarations 6.4 Translation of Expressions 6.5 Type Checking 6.6 Control Flow 6.7 Backpatching 6.8 Switch-Statements 6.9 Intermediate Code for Procedures 6.10 Summary of Chapter 6 6.11 References for Chapter 6 7 Run-Time Environments 7.1 Storage Organization 7.2 Stack Allocation of Space 7.3 Access to Nonlocal Data on the Stack 7.4 Heap Management 7.5 Introduction to Garbage Collection 7.6 Introduction to Trace-Based Collection 7.7 Short-Pause Garbage Collection 7.8 Advanced Topics in Garbage Collection 7.9 Summary of Chapter 7 7.10 References for Chapter 7 8 Code Generation 8.1 Issues in the Design of a Code Generator 8.2 The Target Language 8.3 Addresses in the Target Code 8.4 Basic Blocks and Flow Graphs 8.5 Optimization of Basic Blocks 8.6 A Simple Code Generator 8.7 Peephole Optimization 8.8 Register Allocation and Assignment 8.9 Instruction Selection by Tree Rewriting 8.10 Optimal Code Generation for Expressions 8.11 Dynamic Programming Code-Generation 8.12 Summary of Chapter 8 8.13 References for Chapter 8 9 Machine-Independent Optimizations 9.1 The Principal Sources of Optimization 9.2 Introduction to Data-Flow Analysis 9.3 Foundations of Data-Flow Analysis 9.4 Constant Propagation 9.5 Partial-Redundancy Elimination 9.6 Loops in Flow Graphs 9.7 Region-Based Analysis 9.8 Symbolic Analysis 9.9 Summary of Chapter 9 9.10 References for Chapter 9 10 Instruction-Level Parallelism 10.1 Processor Architectures 10.2 Code-Scheduling Constraints 10.3 Basic-Block Scheduling 10.4 Global Code Scheduling 10.5 Software Pipelining 10.6 Summary of Chapter 10 10.7 References for Chapter 10 11 Optimizing for Parallelism and Locality 11.1 Basic Concepts 11.2 Matrix Multiply: An In-Depth Example 11.3 Iteration Spaces 11.4 Affine Array Indexes 11.5 Data Reuse 11.6 Array Data-Dependence Analysis 11.7 Finding Synchronization-Free Parallelism 11.8 Synchronization Between Parallel Loops 11.9 Pipelining 11.10 Locality Optimizations 11.11 Other Uses of Affine Transforms 11.12 Summary of Chapter 11 11.13 References for Chapter 11 12 Interprocedural Analysis 12.1 Basic Concepts 12.2 Why Interprocedural Analysis? 12.3 A Logical Representation of Data Flow 12.4 A Simple Pointer-Analysis Algorithm 12.5 Context-Insensitive Interprocedural Analysis 12.6 Context-Sensitive Pointer Analysis 12.7 Datalog Implementation by BDD's 12.8 Summary of Chapter 12 12.9 References for Chapter 12 A A Complete Front End A.1 The Source Language A.2 Main A.3 Lexical Analyzer A.4 Symbol Tables and Types A.5 Intermediate Code for Expressions A.6 Jumping Code for Boolean Expressions A.7 Intermediate Code for Statements A.8 Parser A.9 Creating the Front End B Finding Linearly Independent Solutions Index

Compilers: Principles, Techniques, and Tools

From the Publisher:
This book presents the data structures and algorithms that underpin much of today's computer programming. The basis of this book is the material contained in the first six chapters of our earlier work, The Design and Analysis of Computer Algorithms. We have expanded that coverage and have added material on algorithms for external storage and memory management. As a consequence, this book should be suitable as a text for a first course on data structures and algorithms. The only prerequisite we assume is familiarity with some high-level programming language such as Pascal.

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Data Structures and Algorithms

This book goes into the details of database conception and use, it tells you everything on relational databases. from theory to the actual used algorithms.

Principles of database and knowledge-base systems

We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating “implication rules,” which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co-occurrence), and we show how they produce more intuitive results than other methods. Finally, we show how different characteristics of real data, as opposed by synthetic data, can dramatically affect the performance of the system and the form of the results.

https://www.inf.unibz.it/~mkacimi/paperapriori1.pdf

Jeffrey D. Ullman

Papers

Introduction to Automata Theory, Languages, and Computation

Compilers: Principles, Techniques, and Tools

Data Structures and Algorithms

Principles of database and knowledge-base systems

Dynamic itemset counting and implication rules for market basket data