Implementasi Data Mining Clastering Association Prediction Estimation Classification
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Implementasi Data Mining Clastering Association Prediction Estimation Classification
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Author : 1. Rulin Swastika 2. Siti Mukodimah 3. Ferry Susanto 4. Muhamad Muslihudin 5. Sri Ipnuwati Penerbit Adab
language : id
Publisher: Penerbit Adab
Release Date :
Implementasi Data Mining Clastering Association Prediction Estimation Classification written by 1. Rulin Swastika 2. Siti Mukodimah 3. Ferry Susanto 4. Muhamad Muslihudin 5. Sri Ipnuwati Penerbit Adab and has been published by Penerbit Adab this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Judul : IMPLEMENTASI DATA MINING (Clastering, Association, Prediction, Estimation, Classification) Penulis : 1. Rulin Swastika 2. Siti Mukodimah 3. Ferry Susanto 4. Muhamad Muslihudin 5. Sri Ipnuwati Editor : Didi Susianto, M.Kom Suyono, M.T.I Ukuran : 15,5 x 23 cm Tebal : 124 Halaman No ISBN : 978-623-497-285-6 Sinopsis Pada dasarnya keberadaan data mining dibutuhkan mengingat semakin banyaknya informasi di era teknologi seperti data transaksi bisnis, data ilmiah, gambar, video dan data-data lainnya. Dengan banyaknya data tersebut dibutuhkan sistem yang mampu mengekstraksi esensi dari semua informasi yang tersedia dan membuat ringkasan untuk membantu pengambilan keputusan yang lebih baik. Adapun manfaat menggunakan data mining di era digital yaitu adalah: 1. Mengetahui tren pasar 2. Memprediksi keputusan bisnis di masa mendatang 3. Mengetahui produk yang sedang viral 4. Mengamati perilaku konsumen 5. Sarana menyusun strategi peningkatan penjualan Data mining sangatlah berguna bagi pembisnis baik pada level perusahaan atau individu yang mengandalkan data sebagai basis pengambilan keputusan. Data mining dalam dunia bisnis mampu menarik informasi spesifik dari volume data yang besar untuk menemukan solusi bagi masalah bisnis perusahaan. Buku ini terdiri dari 9 Bab yang terdiri dari: BAB I Konsep Dasar Data Mining BAB II Proses Data Mining BAB III Tools Data Mining BAB IV Konsep Data Preprocessing BAB V Clustering Data BAB VI Estimation Data BAB VII Classification Data BAB VIII Prediction Data BAB IX Dataset Pendukung Data Mining
Discovering Knowledge In Data
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Author : Daniel T. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2005-01-28
Discovering Knowledge In Data written by Daniel T. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-28 with Computers categories.
Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Perangkat Lunak Data Mining
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Author : Widyastuti Andriyani
language : id
Publisher: Penerbit Widina
Release Date : 2024-11-04
Perangkat Lunak Data Mining written by Widyastuti Andriyani and has been published by Penerbit Widina this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-04 with Technology & Engineering categories.
Buku "Perangkat Lunak Data Mining" menyajikan teknik dan alat yang digunakan dalam penggalian data untuk membantu pengambil keputusan memahami serta memanfaatkan data dalam cara yang paling efektif. Dengan struktur ini, dapat menambah wawasan tentang bagaimana perangkat lunak data mining beroperasi dan bagaimana memaksimalkannya untuk berbagai aplikasi analitis. Buku ini menjelaskan konsep dasar data mining serta pentingnya perangkat lunak dalam proses pengumpulan dan pra-pemrosesan data. Pembaca akan diajak untuk memahami arsitektur perangkat lunak yang umum digunakan, termasuk berbagai metode dan algoritma yang menjadi andalan, seperti Decision Trees, Random Forest, Support Vector Machines (SVM), K-Means, dan Hierarchical Clustering. Selain itu, buku ini mengupas dua algoritma populer dalam analisis data, yaitu Apriori dan FP-Growth, yang membantu dalam menemukan pola dalam data besar. Buku ini juga membahas tantangan yang sering dihadapi dalam pengembangan perangkat lunak data mining dan memberikan wawasan tentang masa depan data mining, termasuk tren dan perkembangan terbaru dalam teknologi ini. Dengan studi kasus yang mendalam, pembaca dapat melihat aplikasi praktis dari perangkat lunak data mining dalam berbagai sektor industri. Buku ini dirancang untuk menjadi sumber daya yang bermanfaat bagi yang ingin mempelajari Data Mining, dari analis data hingga pengambil keputusan strategis, serta menambah pengetahuan praktis tentang bagaimana memaksimalkan potensi perangkat lunak Data Mining dalam berbagai sektor industri.
Predictive Analytics And Data Mining
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Author : Vijay Kotu
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-11-27
Predictive Analytics And Data Mining written by Vijay Kotu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-27 with Computers categories.
Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples
Data Mining And Analysis
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Author : Mohammed J. Zaki
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-12
Data Mining And Analysis written by Mohammed J. Zaki and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Computers categories.
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Data Mining Teori Praktis Dan Implementasi Menggunakan Weka Dan Bahasa Pemrograman Java
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Author : Dr.Kahf Heryandi Suradiradja. M.Kom.
language : id
Publisher: Penerbit Adab
Release Date :
Data Mining Teori Praktis Dan Implementasi Menggunakan Weka Dan Bahasa Pemrograman Java written by Dr.Kahf Heryandi Suradiradja. M.Kom. and has been published by Penerbit Adab this book supported file pdf, txt, epub, kindle and other format this book has been release on with Technology & Engineering categories.
Judul : DATA MINING : Teori Praktis dan Implementasi Menggunakan WEKA dan Bahasa Pemrograman Java Penulis : Dr.Kahf Heryandi Suradiradja. M.Kom., dan Salman Farizy. S.Kom, M.Kom, MCSE, MVP. Ukuran : 15,5 x 23 Tebal : 132 Halaman Cover : Soft Cover No. ISBN : 978-623-8718-82-5 No. E-ISBN : 978-623-8718-83-2 (PDF) SINOPSIS Buku ini bertujuan untuk memberikan pengantar mengenai konsep dasar, teknik dan aplikasi dari data mining dengan menggunakan aplikasi open source WEKA serta di implementasikan hasil pemodelan dari WEKA tersebut dapat digunakan atau embeded sebagai fungsi atau rule pada pengembangan aplikasi sistem cerdas dengan bahasa pemrograman JAVA. Dengan pemahaman dasar data mining ini, maka diharapkan bahwa pembaca akan mendapatkan pengetahuan juga wawasan tentang bagaimana teknik ini dapat digunakan untuk merubah ‘data mentah’ (raw material) menjadi informasi yang berguna, berharga dan juga dapat diandalkan untuk mengestimasi ataupun memprediksi suatu kasus. Selain itu, tulisan ini juga banyak membahas tentang sejarah, pentingnya dan berbagai bidang aplikasi dari data mining, menunjukkan bagaimana data mining sudah menjadi ‘tools’ yang tidak tergantikan dalam analisis data modern.
Applied Spatial Data Analysis With R
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Author : Roger S. Bivand
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-21
Applied Spatial Data Analysis With R written by Roger S. Bivand and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-21 with Medical categories.
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
Data Mining Applications With R
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Author : Yanchang Zhao
language : en
Publisher: Academic Press
Release Date : 2013-11-26
Data Mining Applications With R written by Yanchang Zhao and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-26 with Computers categories.
Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website. - Helps data miners to learn to use R in their specific area of work and see how R can apply in different industries - Presents various case studies in real-world applications, which will help readers to apply the techniques in their work - Provides code examples and sample data for readers to easily learn the techniques by running the code by themselves
Data Mining
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Author : Mehmed Kantardzic
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-23
Data Mining written by Mehmed Kantardzic and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-23 with Computers categories.
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.
Sustainable Bioeconomy
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Author : V. Venkatramanan
language : en
Publisher: Springer Nature
Release Date : 2020-11-06
Sustainable Bioeconomy written by V. Venkatramanan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-06 with Science categories.
Sustainable development is the most important challenge facing humanity in the 21st century. The global economic growth in the recent past has indeed exhibited marked progress in many countries. Nevertheless, the issues of income disparity, poverty, gender gaps, and malnutrition are not uncommon in the global landscape, in spite of the upward growth of the economy and technological advances. This grim picture is further exacerbated by our growing human population, unmindful resource use, ever-increasing consumption trends, and changing climate. In order to protect humanity and preserve the planet, the United Nations issued the “2030 agenda for sustainable development,” which includes but is not limited to sustainable production and consumption practices, e.g. in a sustainable bioeconomy. The hallmark of the sustainable bioeconomy is a paradigm shift from a fossil-fuel-based economy to a biological-based one, which is driven by the virtues of sustainability, efficient utilization of resources, and “circular economy.” As the sustainable bioeconomy is based on the efficient utilization of biological resources and societal transformations, it holds the immense potential to achieve the UN’s Sustainable Development Goals. This book shares valuable insights into the linkages between the sustainable bioeconomy and Sustainable Development Goals, making it an essential read for policymakers, researchers and students of environmental studies.