Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!)

Data Mining and Anlaytics are the foundation technologies for the new knowledge based world where we build models from data and databases to understand and explore our world. Data mining can improve our business, improve our government, and improve our life and with the right tools, any one can begin to explore this new technology, on the path to becoming a data mining professional. This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built. This is the first step in a journey to data mining and analytics. The book encourages the concept of programming by example and programming with data - more than just pushing data through tools, but learning to live and breathe the data, and sharing the experience so others can copy and build on what has gone before. It is accessible to many readers and not necessarily just those with strong backgrounds in computer science or statistics. Details of some of the more popular algorithms for data mining are very simply and, more importantly, clearly explained. Technology for transforming a database through data mining and machine learning into knowledge is now readily accessible.

Dr Graham Williams is Senior Director of Analytics with the Australian Taxation Office, and previously Principal Computer Scientist for Data Mining with CSIRO. He is also Visiting Professor and Senior International Scientist with the Shenzhen Institutes of Advanced Analytics of the Chinese Academy of Sciences, Adjunct Professor, Data Mining, Fraud Prevention, Security, University of Canberra, and Adjunct Professor, Australian National University. Graham regularly teaches data mining courses and is author of the freely available, open source data mining system, Rattle. He has been involved in many data mining projects for clients from government and industry over his long career. His research developments included ensemble learning (1980's) and hot spots discovery (1990's). He is actively involved in the international artificial intelligence and data mining research communities, particularly as chair of the Pacific Asia Knowledge Discovery and Data Mining conference series and founder and co-chair of the Australasian Data Mining conference series. Graham has editted a number of books and authored many academic and industry papers and reports. His current focus is on making data mining technology readily accessible, ensuring research, innovation and discovery are repeatable and available, and encouraging the free and open sharing of knowledge.