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"This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering.Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail.Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included.This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift."

"Ekstraksi informasi merupakan sebuah tahap awal dari proses analisis data tekstual. Ekstraksi informasi diperlukan untuk mendapatkan informasi dari data tekstual sehingga dapat digunakan untuk proses analisis seperti misalnya klasifikasi dan kategorisasi. Data tekstualsangat dipengaruhi oleh bahasa, jika sebuah data tekstual berbahasa Arab maka karakter yang digunakan adalah karakter arab.Knowledge dictionary merupakan sebuah kamus yang dapat digunakan untuk mengekstraksi informasi dari data tekstual. Informasi yang diekstraksi menggunakan knowledge dictionary adalah konsep.Knowledge dictionary biasanya dibangun secara manual oleh seorang pakar yang tentunya membutuhkan waktu yang lama dan spesifik untuksetiap masalah. Pada penelitian ini diusulkan sebuah metode untuk membangun knowledge dictionary secara otomatis. Pembentukanknowledge dictionary dilakukan dengan cara mengelompokkan kalimat yang memiliki konsep yang sama, dengan asumsi kalimat yang memiliki konsep yang sama akan memiliki nilai simi laritas yang tinggi. Konsep yang telah diekstraksi dapat digunakan sebagai fitur untuk proses komputasi berikutnya misalnya klasifikasi ataupun kategorisasi.Dataset yang digunakan dalam penelitian ini adalah dataset teks Arab. Hasil ekstraksi diuji dengan menggunakan mesin klasifikasidecision tree dan didapatkan nilai presisi tertinggi 71,0% dan nilai recall tertinggi 75,0%.

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AbstractInformation extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention inmany studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledgedictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%. "

"Seorang mahasiswa sebelum melakukan kegiatan akademis di Perguruan Tinggi akan melakukan pengisian Formulir Rencana Studi (FRS). Dengan bertambah majunya teknologi informasi dijital seperti intemet, aplikasi pengisian form FRS dapat dilakukan dad jarak jauh dengan menggunakan world wide web. Pada skripsi ini akan diterapkan aplikasi basis data pads dokumen web di intemet berupa pengisian form Hypertext Markup Language (HTML) dengan perancangan dan pengembangan perangkat lunak untuk sistem pengisian FRS jarak jauh berdasarkan basis data pada Sistem Informasi Basis Data Pakar dan ujicoba pads jaringan komputer lokal intranet. Pengisian form FRS dilakukan oleh mahasiswa yang datanya tercatat pada basis data komputer kampus, dalam hal ini digunakan basis data pada Sistem Informasi Basis Data Pakar (Sibapak). Berdasarkan data kondisi akademis pads basis data, seorang mahasiswa mengisi FRS sesuai dengan syarat Indeks Prestasi pada semester sebelumnya. Pengisian form FRS dengan intemet ditujukan untuk mengatasi permasalahan administrasi yang sering terjadi pada pengisian FRS biasa seperti faktor keterlambatan karena saat liburan, letak kampus yang berada di luar kota atau tersebar."

"These four volumes (CCIS 297, 298, 299, 300) constitute the proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, held in Catania, Italy, in July 2012. The 258 revised full papers presented together with six invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy machine learning and on-line modeling, computing with words and decision making, soft computing in computer vision, rough sets and complex data analysis, theory and applications, intelligent databases and information system, information fusion systems, philosophical and methodological aspects of soft computing, basic issues in rough sets, 40th anniversary of the measures of fuziness, SPS11 uncertainty in profiling systems and applications, handling uncertainty with copulas, formal methods to deal with uncertainty of many-valued events, linguistic summarization and description of data; fuzzy implications, theory and applications, sensing and data mining for teaching and learning, theory and applications of intuitionistic fuzzy sets, approximate aspects of data mining and database analytics, fuzzy numbers and their applications, information processing and management of uncertainty in knowledge-based systems, aggregation functions, imprecise probabilities, probabilistic graphical models with imprecision, theory and applications, belief function theory, basics and/or applications, fuzzy uncertainty in economics and business, new trends in De Finetti's approach, fuzzy measures and integrals, multicriteria decision making, uncertainty in privacy and security, uncertainty in the spirit of Pietro Benvenuti, coopetition; game theory, probabilistic approach."

"Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it. Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment. You will: See the history of AI and how machine learning and cognitive computing evolved Discover why cognitive computing is so important and why your business needs it Master the details of modern AI as it applies to enterprises Map the path ahead in terms of your IT-business integration Avoid common road blocks in the process of adopting cognitive computing in your business."

""It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations"-"

""Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big D"