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Data mining and Warehousing

About Data mining and Warehousing

The fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend it without proper tools. Therefore, this class introduces data and web mining concepts, techniques and applications. More specifically, the course will cover data and web mining tasks, KDD/DM Process Model, step-by-step exploratory data quality analysis, as well as brief reviews of techniques for classification, clustering and pattern discovery. In addition, the course covers the data warehousing which encompasses algorithms and tools for bringing together data from distributed information repositories into a single repository that can be suitable for data analysis and data mining. The content includes: Data Mining: Fundamentals of data mining process and system architecture, relationship with data warehouse and OLAP systems, data pre-processing; Mining Techniques and Application: association rules, Classification, Clustering. Data Warehouse: Data Model for Data Warehouses; Implementing Data Warehouses: data extraction, cleaning, transformation and loading, data cube computation, materialized view selection, OLAP query processing. The student will have practical experience of installing appropriate data mining and data warehousing tools, preprocessing data, Mining associated rules for very large data sets, investigating some hidden patterns using cluster analysis, predicting using classification analysis.

Course Highlights

The videos section of this course features a selection of video lectures and interviews of Data mining and Warehousing faculty from various Departments at KIoT.