Sistem Informasi Pustaka

Transparent Data Mining for Big and Small Data

Machine Learning and Data Mining

Introduction to Principles and Algorithms

Data Mining with Computational Intelligence

Applied Data Mining for Forecasting Using SAS(R)

Temporal and Spatio-Temporal Data Mining

Cluster Analysis for Data Mining and System Identification

Learning Classifier Systems in Data Mining

The ability of Learning Classifier Systems (LCS) to solve complex real-world problems is becoming clear. This book brings together work by a number of individuals who demonstrate the good performance
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Advanced Data Mining Techniques

Geographic Data Mining and Knowledge Discovery, Second Edition

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

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Data Mining and Warehousing

Data Mining is the process of analyzing large amount of data in search of previously undiscovered business patterns. Data Warehousing is a relational/multidimensional database that is designed for Query and Analysis rather than Transaction Processing. This book provides a systematic introduction to the principles of Data Mining and Data Warehousing. It covers the entire range of data mining algorithms (prediction, classification, and association), data mining products and applications, stages.

S. Prabhu,

Data Warehousing is a relational/multidimensional database that is designed for Query and Analysis rather than Transaction Processing. This book provides a systematic introduction to the principles of Data Mining and Data Warehousing.

Transparent Data Mining for Big and Small Data

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches.As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to practical use.

Tania Cerquitelli, Daniele Quercia, Frank Pasquale,

This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions.

Machine Learning and Data Mining

Introduction to Principles and Algorithms

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

Igor Kononenko, Matjaž Kukar,

Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer ...

Data Mining with Computational Intelligence

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.

Lipo Wang, Xiuju Fu,

The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.

Sequence Data Mining

Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.

Guozhu Dong, Jian Pei,

Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.

Applied Data Mining for Forecasting Using SAS(R)

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.

Tim Rey, Arthur Kordon, Chip Wells,

Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.

Temporal and Spatio-Temporal Data Mining

"This book presents probable solutions when discovering the spatial sequence patterns by incorporating the information into the sequence of patterns, and introduces new classes of spatial sequence patterns, called flow and generalized spatio-temporal patterns, addressing different scenarios in spatio-temporal data by modeling them as graphs, providing a comprehensive synopsis on two successful partition-based algorithms designed by the authors"--Provided by publisher.

Hsu, Wynne,

Cluster Analysis for Data Mining and System Identification

This book presents new approaches to data mining and system identification. Algorithms that can be used for the clustering of data have been overviewed. New techniques and tools are presented for the clustering, classification, regression and visualization of complex datasets. Special attention is given to the analysis of historical process data, tailored algorithms are presented for the data driven modeling of dynamical systems, determining the model order of nonlinear input-output black box models, and the segmentation of multivariate time-series. The main methods and techniques are illustrated through several simulated and real-world applications from data mining and process engineering practice. The book is aimed primarily at practitioners, researches, and professionals in statistics, data mining, business intelligence, and systems engineering, but it is also accessible to graduate and undergraduate students in applied mathematics, computer science, electrical and process engineering. Familiarity with the basics of system identification and fuzzy systems is helpful but not required. Key features: - Detailed overview of the most powerful algorithms and approaches for data mining and system identification is presented. - Extensive references give a good overview of the current state of the application of computational intelligence in data mining and system identification, and suggest further reading for additional research. - Numerous illustrations to facilitate the understanding of ideas and methods presented. - Supporting MATLAB files, available at the website www.fmt.uni-pannon.hu/softcomp create a computational platform for exploration and illustration of many concepts and algorithms presented in the book.

Janos Abonyi, Balázs Feil,

This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes. This book presents new approaches to data mining and system identification.

Learning Classifier Systems in Data Mining

Larry Bull,

Advanced Data Mining Techniques

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining.

David L. Olson, Dursun Delen,

Geographic Data Mining and Knowledge Discovery, Second Edition

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and Spatiotemporal Databases Since the publication of the first edition of Geographic Data Mining and Knowledge Discovery, new techniques for geographic data warehousing (GDW), spatial data mining, and geovisualization (GVis) have been developed. In addition, there has been a rise in the use of knowledge discovery techniques due to the increasing collection and storage of data on spatiotemporal processes and mobile objects. Incorporating these novel developments, this second edition reflects the current state of the art in the field. New to the Second Edition Updated material on geographic knowledge discovery (GKD), GDW research, map cubes, spatial dependency, spatial clustering methods, clustering techniques for trajectory data, the INGENS 2.0 software, and GVis techniques New chapter on data quality issues in GKD New chapter that presents a tree-based partition querying methodology for medoid computation in large spatial databases New chapter that discusses the use of geographically weighted regression as an exploratory technique New chapter that gives an integrated approach to multivariate analysis and geovisualization Five new chapters on knowledge discovery from spatiotemporal and mobile objects databases Geographic data mining and knowledge discovery is a promising young discipline with many challenging research problems. This book shows that this area represents an important direction in the development of a new generation of spatial analysis tools for data-rich environments. Exploring various problems and possible solutions, it will motivate researchers to develop new methods and applications in this emerging field.

Harvey J. Miller, Jiawei Han,

Incorporating these novel developments, this second edition reflects the current state of the art in the field.

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you’ll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system. * Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems. * Helps you to understand the trade-offs implicit in various models and model architectures. * Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction. * Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model. * In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem. * Presents examples in C, C++, Java, and easy-to-understand pseudo-code. * Extensive online component, including sample code and a complete data mining workbench.

Earl Cox,

You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects.

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