| Modern hardware can easily collect megabytes of data from various sources within a short period of time. This explosive growth in data has overwhelmed analysts for years. To overcome the problem of information overloading, data mining has emerged as a major frontier. Data mining is the automated extraction of regularities and patterns representing previously unknown knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. In this course, we will discuss suitable data models, data preparation, and finally, different methods and algorithms to discover new knowledge from raw data. Major topics include: (1) Data warehousing and data cleansing, (2) Decision tree classification and customer behavior prediction, (3) Data clustering, (4) Association rule and market basket analysis, (5) Temporal sequence and spatial trend analysis, (6) Data mining tools and frameworks, (7)Inductive and analytical learning, and (8) Genetic algorithms and programming. This course is ideal for anyone who needs to learn how to analyze raw data to maximize strategic planning, marketing power, and bottom-line success. Prerequisite: SEIS630 and programming experience |
