Data Mining is widely used in business and various sectors to enable organizations and individuals make better decisions and improve processes, systems and outcomes. This course is a continuation of Data Mining and Wrangling (DMW) and includes three main topics: advanced cluster analysis, outlier analysis and recommender systems. Cluster analysis will be revisited to include density-and grid-based clustering algorithms as well as scalable and high-dimensional clustering. The fundamentals of outlier analysis, a useful technique for many frequently encountered problems, will be discussed along with its applications. This course also introduces recommender systems that go beyond what is discussed in BDCC: model-based, content-based and knowledge-based recommender systems. By the end of the course, students are expected to present their completed project that would make use of the topics discussed in the course.