Data mining and Data Warehousing in Databases.pptx
- 1. PRESENTATION BY - M Awais :)) - Taha - Ibrahim - M Ahmed
- 2. Data mining Definition: Data mining in databases is the process of analyzing large sets of data stored in a database to find patterns, trends, and useful information. Why Important? Without data mining, large amounts of data stored in databases might go underused •.
- 3. Key Concepts in Data Mining Data Warehousing: Centralized data storage from different sources for analysis. Database Queries: Using SQL to retrieve specific data for mining. Data Preprocessing: Cleaning data to ensure it is usable for mining (removing errors or irrelevant info). Mining Algorithms: Tools used to analyze data, like classification, clustering, and association rules.
- 4. Data Mining Techniques in Databases 1. Classification: Organizing data into predefined categories. Example: Categorizing customer data into different segments (e.g., age groups). 2. Clustering: Grouping data into clusters based on similarities. Example: Grouping customers by purchasing behavior without predefined labels. 3. Association Rule Mining: Discovering relationships between data. Example: If a customer buys a laptop, they might also buy a mouse. 4. Regression: Predicting values based on existing data. Example: Predicting the future sales of a product based on past data.
- 5. Data Mining Process in Databases Data Selection: Choosing relevant data from the database. Data Cleaning: Removing errors or irrelevant data from the database. Data Transformation: Converting data into a suitable format for mining. Data Mining: Applying mining techniques to discover patterns. Pattern Evaluation: Assessing the usefulness of discovered patterns. Knowledge Representation: Presenting the results of data mining in an understandable way.
- 6. Challenges of Data Mining Data Quality Issues: Incomplete, noisy, or inconsistent data can lead to wrong results. Scalability: Mining large databases with millions of records can be resource-intensive. Privacy Concerns: Mining personal data requires ethical handling and user consent. Complexity: Finding meaningful patterns in huge datasets can be difficult.
- 8. What is Data Warehousing? ■ A data warehouse is a system that collects and stores large amounts of data from different sources in one place. It's organized to make it easy to analyze the data and create reports, helping businesses make better decisions.
- 9. Key characteristics ■ Organized for Fast Answers Data is structured to make searching and analyzing quick and easy. ■ Handles Large Data Can store and process a huge amount of data from many sources ■ Consistent Data Ensures all data is cleaned and formatted the same way, so everyone gets reliable results. ■ Non-Volatile Once data is entered into the warehouse, it is not altered or deleted.
- 10. Components of data warehouse ■ Data Sources ■ ETL/ELT Process Extract: Transform Load ■ Data Storage ■ Metadata ■ Access Tools
- 11. Architectures of data warehouse ■ Single-Tier Architecture Everything is in one place ■ Two-Tier Architecture One layer stores and manages the data. Another layer is for users to access and analyze the data. ■ Three-Tier Architecture Bottom Tier: Data storage Middle Tier: Processing layer Top Tier: User interface