DATA MINING CONCEPTS AND TECHNIQUES.pptx

DATA MINING CONCEPTS AND TECHNIQUES
Warehouse
OLAP

1. DATA MINING CONCEPTS AND TECHNIQUES UNIT – I

2. Data mining process of discovering patterns, trends, and insights from large datasets using various statistical, mathematical, and machine learning techniques. It involves extracting meaningful information from data and uncovering hidden relationships or patterns that can be used for decision-making and prediction. Here are some key concepts related to data mining.

3. APPLICATIONS OF DATA MINING Marketing and customer relationship management (CRM) Fraud detection and risk management Healthcare and medical diagnosis Financial analysis and forecasting Recommender systems and personalized recommendations Predictive maintenance and quality control

4. Data Warehouse A data warehouse is a centralized repository that stores integrated, historical data from multiple sources within an organization. It is optimized for querying and analysis rather than transaction processing. The primary purpose of a data warehouse is to provide a unified view of enterprise data, enabling decision-makers to analyze trends, identify patterns, and make informed decisions based on historical and current data.

5. OLAP Technology OLAP (Online Analytical Processing) is a technology that enables users to interactively analyze multidimensional data from different perspectives. It provides capabilities for fast, complex analytical queries and supports advanced data visualization techniques. Multidimensional Analysis: OLAP cubes organize data into multidimensional structures, where data can be viewed and analyzed across multiple dimensions (e.g., time, geography, product category). This allows users to drill down, roll up, pivot, and slice-and-dice data dynamically. Types of OLAP:   ROLAP (Relational OLAP): ROLAP systems store data in relational databases and perform OLAP operations using SQL queries. MOLAP (Multidimensional OLAP): MOLAP systems store data in multidimensional cubes, providing fast query performance and advanced analytics capabilities. HOLAP (Hybrid OLAP): HOLAP systems combine features of both ROLAP and MOLAP, allowing users to store summary data in multidimensional cubes and detailed data in relational databases. Querying and Analysis: OLAP technology supports complex analytical queries, including slice-and-dice, drill down, roll up, pivot, and drill through. Users can explore data interactively, visualize trends, and gain insights into business performance.

6. Data Warehouse Modelling Identify Business Requirements: The first step in data warehouse modeling is to understand the business requirements and objectives. This involves collaborating with business stakeholders to determine the key metrics, dimensions, and business processes that need to be analyzed. Identify Dimensions and Facts: Dimensions represent the descriptive attributes by which users want to analyze data (e.g., time, geography, product, customer), while facts represent the measurable metrics or numerical values that users want to analyze (e.g., sales revenue, quantity sold, profit). Design Dimensional Model: The dimensional model organizes data into dimension tables and fact tables. Dimension tables contain attributes describing the dimensions, while fact tables contain numerical values associated with the dimensions. Common dimensional modeling techniques include star schema and snowflake schema. Star Schema: In a star schema, data is organized into a central fact table surrounded by dimension tables. The fact table contains primary keys of dimension tables as foreign keys along with the measures. This simple structure makes queries easy to write and understand. Snowflake Schema: A snowflake schema is a variation of the star schema where dimension tables are normalized into multiple related tables. While it reduces redundancy and improves data integrity, it can increase query complexity.

7. Data Warehouse Modelling Bridge Tables: Bridge tables, also known as many-to-many relationship tables, are used to handle complex relationships between dimensions. They are often required when a single fact can be associated with multiple values in a dimension. Design Aggregations: Aggregations are pre-calculated summaries of data that improve query performance in the data warehouse. They are typically stored in separate aggregate tables and are used to speed up queries for commonly accessed reports and dashboards. Define Hierarchies: Hierarchies define the relationships between different levels of attributes within a dimension. They enable users to drill up (roll up) or drill down (drill down) through the data to analyze it at different levels of granularity. Data Cleansing and Validation: Before loading data into the data warehouse, it's essential to cleanse and validate it to ensure its quality and consistency. This involves identifying and correcting errors, handling missing values, and ensuring data conforms to the defined schema. Iterative Process: Data warehouse modeling is often an iterative process that involves continuous refinement based on feedback from users and changes in business requirements. It's essential to regularly review and update the dimensional model to ensure it remains aligned with the organization's evolving needs.

8. Data Cube And OLAP

9. Data warehouse implementation

10. Data preprocessing

11. Data cleaning Data integration Data reduction

12. Data transformation Data discretization