Sistem Informasi Pustaka

Swarm Intelligence in Data Mining

The Top Ten Algorithms in Data Mining

Soft Computing for Knowledge Discovery and Data Mining

Data Mining: Concepts and Techniques

Implementing Data Mining Algorithms in Microsoft SQL Server

"All source codes, as well as data sets used in computational experiments, are included in the accompanying CD-ROM." -- p. [xv]

Data Preprocessing in Data Mining

Data Mining and Knowledge Discovery for Big Data

Methodologies, Challenge and Opportunities

Exploratory Data Mining and Data Cleaning

Data Mining Algorithms

Explained Using R

"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Sebanyak 44165 item atau buku ditemukan

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Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: A general overview of the discipline, from its foundations to contemporary industrial applications Chemoinformatics-based applications Bioinformatics-based applications Data mining methods in clinical development Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

Konstantin V. Balakin,

This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery—including chemogenomics, toxicogenomics, and individual drug ...

Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management

The leading introductory book on data mining, fully updated andrevised! When Berry and Linoff wrote the first edition of Data MiningTechniques in the late 1990s, data mining was just starting tomove out of the lab and into the office and has since grown tobecome an indispensable tool of modern business. This newedition—more than 50% new and revised— is asignificant update from the previous one, and shows you how toharness the newest data mining methods and techniques to solvecommon business problems. The duo of unparalleled authors shareinvaluable advice for improving response rates to direct marketingcampaigns, identifying new customer segments, and estimating creditrisk. In addition, they cover more advanced topics such aspreparing data for analysis and creating the necessaryinfrastructure for data mining at your company. Features significant updates since the previous edition andupdates you on best practices for using data mining methods andtechniques for solving common business problems Covers a new data mining technique in every chapter along withclear, concise explanations on how to apply each techniqueimmediately Touches on core data mining techniques, including decisiontrees, neural networks, collaborative filtering, association rules,link analysis, survival analysis, and more Provides best practices for performing data mining using simpletools such as Excel Data Mining Techniques, Third Edition covers a new datamining technique with each successive chapter and then demonstrateshow you can apply that technique for improved marketing, sales, andcustomer support to get immediate results.

Gordon S. Linoff, Michael J. A. Berry, Michael J. A. Berry, Michael J. A. Berry,

This third edition of Data Mining Techniques covers such topics as: How to create stable, long-lasting predictive models Data preparation and variable selection Modeling specific targets with directed techniques such as regression, decision ...

Swarm Intelligence in Data Mining

This volume examines the application of swarm intelligence in data mining, addressing the issues of swarm intelligence and data mining using novel intelligent approaches. The book comprises 11 chapters including an introduction reviewing fundamental definitions and important research challenges. Important features include a detailed overview of swarm intelligence and data mining paradigms, focused coverage of timely, advanced data mining topics, state-of-the-art theoretical research and application developments and contributions by pioneers in the field.

Ajith Abraham, Crina Grosan, Vitorino Ramos,

This book deals with the application of swarm intelligence in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume.

The Top Ten Algorithms in Data Mining

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

Xindong Wu, Vipin Kumar,

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications.

Soft Computing for Knowledge Discovery and Data Mining

Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.

Oded Maimon, Lior Rokach,

It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently.

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. 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 your data

Jiawei Han, Jian Pei, Micheline Kamber, Jian Pei, Micheline Kamber, Jian Pei, Micheline Kamber, Jian Pei, Micheline Kamber, Jian Pei, Micheline Kamber,

Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD).

Applied Data Mining

Statistical Methods for Business and Industry

Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance. This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems. Provides a solid introduction to applied data mining methods in a consistent statistical framework Includes coverage of classical, multivariate and Bayesian statistical methodology Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real life applications Features a number of detailed case studies based on applied projects within industry Incorporates discussion on software used in data mining, with particular emphasis on SAS Supported by a website featuring data sets, software and additional material Includes an extensive bibliography and pointers to further reading within the text Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.

Paolo Giudici,

This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice.

Implementing Data Mining Algorithms in Microsoft SQL Server

"All source codes, as well as data sets used in computational experiments, are included in the accompanying CD-ROM." -- p. [xv]

Claudio Luiz Curotto, Nelson F. F. Ebecken,

"All source codes, as well as data sets used in computational experiments, are included in the accompanying CD-ROM." -- p. [xv]

Data Preprocessing in Data Mining

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Salvador García, Julián Luengo, Francisco Herrera,

Abstract KEEL software is an open source Data Mining tool widely used in
research and real life applications. Most of the algorithms described, if not all of
them, throughout the book are actually implemented and publicly available in this
Data ...

Data Mining and Knowledge Discovery for Big Data

Methodologies, Challenge and Opportunities

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Wesley W. Chu,

Opinion mining or sentiment analysis is the computational study of people's
opinions, appraisals, attitudes, and emotions toward entities such as products,
services, organizations, individuals, events, and their different aspects. It has
been an ...

Exploratory Data Mining and Data Cleaning

Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

Tamraparni Dasu, Theodore Johnson,

Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level ...

Data Mining Algorithms

Explained Using R

"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Pawel Cichosz,

"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

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Sistem Informasi Pustaka

Pelita Bangsa

Pharmaceutical Data Mining

Approaches and Applications for Drug Discovery

Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management

Swarm Intelligence in Data Mining

The Top Ten Algorithms in Data Mining

Soft Computing for Knowledge Discovery and Data Mining

Data Mining: Concepts and Techniques

Applied Data Mining

Statistical Methods for Business and Industry

Implementing Data Mining Algorithms in Microsoft SQL Server

Data Preprocessing in Data Mining

Data Mining and Knowledge Discovery for Big Data

Methodologies, Challenge and Opportunities

Exploratory Data Mining and Data Cleaning

Data Mining Algorithms

Explained Using R

Sebanyak 44165 item atau buku ditemukan

Pharmaceutical Data Mining

Approaches and Applications for Drug Discovery

Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management

Swarm Intelligence in Data Mining

The Top Ten Algorithms in Data Mining

Soft Computing for Knowledge Discovery and Data Mining

Data Mining: Concepts and Techniques

Applied Data Mining

Statistical Methods for Business and Industry

Implementing Data Mining Algorithms in Microsoft SQL Server

Data Preprocessing in Data Mining

Data Mining and Knowledge Discovery for Big Data

Methodologies, Challenge and Opportunities

Exploratory Data Mining and Data Cleaning

Data Mining Algorithms

Explained Using R