Knowledge discovery is defined as a process of the meaningful pattern extraction of implicit, unknown, and potentially useful information from data. Knowledge Discovery is very useful and important to find and analyze meaningful patterns in huge and complex database. It provides the capability to discover new and meaningful information by using existing data. It quickly exceeds the human capacity to analyze enormous data sets. Moreover the processing time and result validity to discover knowledge among large data should be deeply considered. The use of intelligent learning techniques in Knowledge Discovery can be useful to retrieve meaningful result in an automated process. The intelligent learning techniques for classification and clustering are required to deal with supervised and unsupervised data among amounts of data.

The course brings the students to understand how to discover meaningful knowledge from enormous sources, to learn the steps of the knowledge discovery process, and to familiarize them with concepts, techniques and tools used in the process. This course is designed to learn Knowledge Discovery from preprocessing stages for data preparation, data selection and transformation, algorithms for mining data, data interpretation, and applicability for several fields.

• Data Pre-processing
• Classification with Decision Tree
• Clustering and Cluster Analysis
• Advanced clustering techniques (Automatic clustering, Incremental clustering, Shape-independent Clustering)
• Information Retrieval (Text Mining, Image Retrieval, Video Retrieval)
• Correlation measurement
• Outlier analysis and anomaly detection
• Multiband image clustering