Data Warehousing and Mining

Unit I: Unit II: Unit III: Unit IV: Unit V: Unit VI: Download PDF HERE

Que 1. Explain the Frequent Pattern Analysis, its important with example. Frequent Pattern Analysis: Importance of Frequent Pattern Analysis: Example: Support: Confidence: Que 2. Explain the Apriori Algorithm in Frequent Itemset Mining Method with example. Apriori Algorithm: The Core of the Apriori Algorithm Apriori algorithm is a sequence of steps, including : Join step: Pruning

Que 1. Explain Data Cube Computation: Preliminary ConceptsExplain Cube Materialization: 1. Full Cube 2. Iceberg cube 3. Closed Cube 4. Cube Shell. Data Cube : Data Cube Computation : Why data cube computation is needed? Cube Materialization (pre-computation): Different Data Cube materialization include. 1. The Full cube: Advantage: Disadvantage: 2. An Iceberg-Cube: Advantage: 3. A

Que 1. Describe the architecture of data warehouse with proper diagram. Data Warehouse: Bottom Tier (Data sources and data storage): Middle Tier: Top Tier: Que 2. Differences between Operational Database Systems and Data Warehouse. Operational Database Systems: Data Warehouse: Sr.no. OLAP OLTP 1. OLAP referred to online analyticalprocessing. OLTP referred to online transactionalprocessing. 2. It

Que 1. Define data pre-processing and need of data pre-processing. Data pre-processing Data preprocessing is an important step in the data mining process. It refers to the cleaning, integrating, reducing, transforming, and discretization of data in order to make it ready for analysis. The goal of data preprocessing is to improve the quality of the

Que 1. What is an attribute? Explain the different types of attributes. Attribute : An attribute is a data field, representing a characteristic or feature of a data object. The nouns attribute, dimension, feature, and variable are often used interchangeably in the literature. The term dimension is commonly used in data warehousing. Machine learning literature

Que 1. What is data mining? Why data mining is required? Data Mining : Why data mining is required ? I. Data Mining Helps Understand Customers Behavior: II. Data Mining Identifies New Opportunities: III. Data Mining Helps Understand What Customers Want: IV. Data Mining Creates Differentiating Products And Services: V. Data Mining Reveals Market Trends: