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Longman Business English Dictionary

Do you need to explain macroeconomics in the classroom? Would your students know what a hurdle rate is?This new edition of the Longman Business English Dictionary gives students an in-depth knowledge of all the vocabulary they need to survive in today's fast-paced business environment, whether they are students of business English or people already in work. You and your students will find it easy to understand complex business terms because all definitions are written using just 2000 common words, making even the most difficult business jargon clear and easy to understand. Make sure you know the latest buzz-words - this fully revised edition is completely up-to-date. Students learn real-world business English from thousands of example sentences which are taken from authentic business English sources. Improve your students' chances of success in the BEC and BULATS exams, by introducing them to the interactive exam practice on the CD-ROM.

Make sure you know the latest buzz-words - this fully revised edition is completely up-to-date. Students learn real-world business English from thousands of example sentences which are taken from authentic business English sources.

Handbook Data Warehouse : Teori dan Praktik Berbasiskan Open Source

Principles of Data Mining

The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections.