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"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."

"This book focuses on mobile data and its applications in the wireless networks of the future. Several topics form the basis of discussion, from a mobile data mining platform for collecting mobile data, to mobile data processing, and mobile feature discovery. Usage of mobile data mining is addressed in the context of three applications: wireless communication optimization, applications of mobile data mining on the cellular networks of the future, and how mobile data shapes future cities."

"This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital."

"Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics."

"Extracting and applying knowledge from chemical, biological, and clinical data is one of the biggest problems for the pharmaceutical industry. Focusing on diverse data mining approaches for drug discovery, including chemogenomics, toxicogenomics, and individual drug response prediction, Pharmaceutical Data Mining links theory to applications to illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development. The book is of vital interest to pharmaceutical scientists, principal investigators and scientists, research directors, industrial."

"The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. "

"Data mining is one of the most rapidly growing research areas in computer science and statistics. Areas of application covered are diverse and include healthcare and finance. We wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field."

"Kompleksitas proses pengadaan dalam aplikasi e-procurement diperlukan performa yang menunjang untuk meningkatkan daya saing. Aplikasi e-procurement berbasis web yang dikembangkan pada sebuah perusahaan, akan menghasilkan data log yang terus bertambah, namun belum dimanfaatkan dengan maksimal. Metode web log mining yang digunakan dalam penelitian ini adalah sequential pattern dengan framework PrefixSpan kemudian dilakukan penghitungan waktu rata-rata yang dihabiskan pada sebuah halaman sesuai sequence pattern yang terbentuk. Hasil pengolahan dan analisis menunjukkan bahwa pengembangan sistem evaluasi tersebut menghasilkan pola urutan yang kemudian digunakan untuk mengetahui urutan akses dan waktu akses antar sequence sebagai bahan analisis untuk melakukan perbaikan atau peningkatan produk. Evaluasi yang dihasilkan adalah indikasi perlunya dilakukan peninjauan query pada bagian dengan waktu memuat halaman lebih dari 10 detik serta penggabungan halaman yang memanggil log bersamaan atau simultan.

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The complexity of the procurement process in e-procurement applications requires performance that supports increasing competitiveness. A web-based e-procurement application developed by a company will produce log data that continues to grow, but has not been utilized to its full potential. The web log mining method used in this study is a sequential pattern with the PrefixSpan framework and then the average time spent on a page is calculated according to the sequence pattern formed. The findings show that the development of the evaluation system produces a sequence of patterns which is then used to determine the order of access and the access time between sequences as an analytical material to make product improvements. The resulting evaluation is an indication of the need to review queries on sections with page loading times greater than 10 seconds and merge pages that call the same log simultaneously.

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