Description
The demand for skilled data scientists is rapidly increasing as more organizations recognize the value of data-driven decision- making. Data science, data management, and data mining are all critical components for various types of organizations, including large and small corporations, academic institutions, and government entities. For companies, these components serve to extract insights and value from their data, empowering them to make evidence-driven decisions and gain a competitive advantage by discovering patterns and trends and avoiding costly mistakes. Academic institutions utilize these tools to analyze large datasets and gain insights into various scientific fields of study, including genetic data, climate data, financial data, and in the social sciences they are used to analyze survey data, behavioral data, and public opinion data. Governments use data science to analyze data that can inform policy decisions, such as identifying areas with high crime rates, determining which regions need infrastructure development, and predicting disease outbreaks. However, individuals who are not data science experts, but are experts within their own fields, may need to apply their experience to the data they must manage, but still struggle to expand their knowledge of how to use data mining tools such as RapidMiner software.
Principles and Theories of Data Mining With RapidMiner is a comprehensive guide for students and individuals interested in experimenting with data mining using RapidMiner software. This book takes a practical approach to learning through the RapidMiner tool, with exercises and case studies that demonstrate how to apply data mining techniques to real-world scenarios. Readers will learn essential concepts related to data mining, such as supervised learning, unsupervised learning, association rule mining, categorical data, continuous data, and data quality. Additionally, readers will learn how to apply data mining techniques to popular algorithms, including k-nearest neighbor (K-NN), decision tree, naïve bayes, artificial neural network (ANN), k-means clustering, and probabilistic methods. By the end of the book, readers will have the skills and confidence to use RapidMiner software effectively and efficiently, making it an ideal resource for anyone, whether a student or a professional, who needs to expand their knowledge of data mining with RapidMiner software.
Author's/Editor's Biography
Sarawut Ramjan
Sarawut Ramjan
is a highly accomplished academician with a Ph.D. in Computer and Engineering Management, which he earned from Assumption University in 2013. In 2017, he joined the College of Innovation at Thammasat University as an Associate Professor, where he currently conducts cutting-edge research at the Thammasat University AI Center. Dr. Ramjan is a renowned expert in data science, with a strong focus on teaching and research in this field. His remarkable contributions to the academic world have made him a prominent figure in the academic community.
Jirapon Sunkpho
Jirapon Sunkpho
is a distinguished scholar with a passion for academic excellence. He holds a Ph.D. in Civil and Environmental Engineering (Computer-Aided Engineering) from the esteemed Carnegie Mellon University, earned in 2001, as well as an MSc in Applied Data Science and Analytics from Technological University Dublin, which he completed in 2021. Dr. Sunkpho is a visionary leader and currently serves as the Vice Rector in Information Technology at Thammasat University, where he plays a key role in shaping the institution's technological direction. He is also a Board of Director at Airport of Thailand, where his expertise is valued. As an accomplished Associate Professor, Dr. Sunkpho is widely recognized for his teaching excellence and his research in data science. His contributions to this field have been profound, and he remains committed to advancing knowledge in this area. With his unique combination of academic prowess and leadership abilities, Dr. Sunkpho is a true asset to the academic and professional communities.