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Data Mining with Ontologies: Implementations, Findings, and Frameworks

Implementations, Findings, and Frameworks

Data Mining and Statistics for Decision Making

Decomposition Methodology for Knowledge Discovery and Data Mining

Data Mining with Decision Trees

Advances in Knowledge Discovery and Data Mining, Part II

14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings

This book constitutes the proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, held in Hyderabad, India, in June 2010.

Strategic Advancements in Utilizing Data Mining and Warehousing Technologies: New Concepts and Developments

"This book presents and disseminates new concepts and developments in the areas of data warehousing and data mining, in particular on the research trends shaped during the last few years"--Provided by
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Data Mining the Web

Uncovering Patterns in Web Content, Structure, and Usage

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics),
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Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

State-of-the-Art and Future Challenges

Intelligent Data Mining

Techniques and Applications

R and Data Mining

Examples and Case Studies

Data Mining in Large Sets of Complex Data

Design and Implementation of Data Mining Tools

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Data Mining with Ontologies: Implementations, Findings, and Frameworks

Implementations, Findings, and Frameworks

"Prior knowledge in data mining is helpful for selecting suitable data and mining techniques, pruning the space of hypothesis, representing the output in a comprehensible way, and improving the overall method. This book examines methodologies and research for the development of ontological foundations for data mining to enhance the ability of ontology utilization and design"--Provided by publisher.

Nigro, Hector Oscar,

This book examines methodologies and research for the development of ontological foundations for data mining to enhance the ability of ontology utilization and design"--Provided by publisher.

Data Mining and Statistics for Decision Making

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Stéphane Tufféry,

This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, ...

Decomposition Methodology for Knowledge Discovery and Data Mining

Theory and Applications

Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery in Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools. The various elements are then joined together to solve the initial problem.The benefits of decomposition methodology in data mining include: increased performance (classification accuracy); conceptual simplification of the problem; enhanced feasibility for huge databases; clearer and more comprehensible results; reduced runtime by solving smaller problems and by using parallel/distributed computation; and the opportunity of using different techniques for individual sub-problems.

Oded Z. Maimon, Lior Rokach,

This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular.

Data Mining with Decision Trees

Theory and Applications

This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

Lior Rokach, Oded Z. Maimon,

Advances in Knowledge Discovery and Data Mining, Part II

14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings

This book constitutes the proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, held in Hyderabad, India, in June 2010.

Mohammed J. Zaki,

This book constitutes the proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, held in Hyderabad, India, in June 2010.

Strategic Advancements in Utilizing Data Mining and Warehousing Technologies: New Concepts and Developments

New Concepts and Developments

Taniar, David,

Data Mining the Web

Uncovering Patterns in Web Content, Structure, and Usage

Zdravko Markov, Daniel T. Larose,

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

State-of-the-Art and Future Challenges

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

Andreas Holzinger, Igor Jurisica,

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information.

Intelligent Data Mining

Techniques and Applications

"Intelligent Data Mining – Techniques and Applications" is an organized edited collection of contributed chapters covering basic knowledge for intelligent systems and data mining, applications in economic and management, industrial engineering and other related industrial applications. The main objective of this book is to gather a number of peer-reviewed high quality contributions in the relevant topic areas. The focus is especially on those chapters that provide theoretical/analytical solutions to the problems of real interest in intelligent techniques possibly combined with other traditional tools, for data mining and the corresponding applications to engineers and managers of different industrial sectors. Academic and applied researchers and research students working on data mining can also directly benefit from this book.

Da Ruan, Guoqing Chen, Etienne E. Kerre, Geert Wets,

The main objective of this book is to gather a number of peer-reviewed high quality contributions in the relevant topic areas.

R and Data Mining

Examples and Case Studies

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Yanchang Zhao,

Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and ...

Data Mining in Large Sets of Complex Data

The amount and the complexity of the data gathered by current enterprises are increasing at an exponential rate. Consequently, the analysis of Big Data is nowadays a central challenge in Computer Science, especially for complex data. For example, given a satellite image database containing tens of Terabytes, how can we find regions aiming at identifying native rainforests, deforestation or reforestation? Can it be made automatically? Based on the work discussed in this book, the answers to both questions are a sound “yes”, and the results can be obtained in just minutes. In fact, results that used to require days or weeks of hard work from human specialists can now be obtained in minutes with high precision. Data Mining in Large Sets of Complex Data discusses new algorithms that take steps forward from traditional data mining (especially for clustering) by considering large, complex datasets. Usually, other works focus in one aspect, either data size or complexity. This work considers both: it enables mining complex data from high impact applications, such as breast cancer diagnosis, region classification in satellite images, assistance to climate change forecast, recommendation systems for the Web and social networks; the data are large in the Terabyte-scale, not in Giga as usual; and very accurate results are found in just minutes. Thus, it provides a crucial and well timed contribution for allowing the creation of real time applications that deal with Big Data of high complexity in which mining on the fly can make an immeasurable difference, such as supporting cancer diagnosis or detecting deforestation.

Robson Leonardo Ferreira Cordeiro, Christos Faloutsos, Caetano Traina Júnior,

Terabyte-scale data mining ... In such cases, the data are already stored on
multiple disks, R. L. F. Cordeiro et al., Data Mining in Large Sets of Complex Data
, 69 SpringerBriefs in Computer Science, DOI: 10.1007/978-1-4471-4890-6_5, ©
The ...

Design and Implementation of Data Mining Tools

Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject. The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification. Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.

Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang,

The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications.

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