{"product_id":"9789390770120-5","title":"Data Warehousing \u0026 Data Mining for  GTU 18 Course  (VI- IT\/Prof. Elec.-III - 3161610) (2027)","description":"\u003cp\u003e1.    Data Warehousing : OLAP and OLTP, Data warehouse and Data mart, OLAM architecture, Extraction, Transform and Loading (ETL) concept for generic, two-tier, three - tier architecture, Data warehousing schema - Star, Snowflake, Fact Constellation (Galaxy) - Data Cube , Operations on Data cube (slicing, roll up, roll down, drill up etc). (Chapter - 1) 2.    Introduction to data mining (DM) : Motivation for Data Mining - Data Mining-Definition and Functionalities – Classification of DM Systems - DM task primitives - Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM – KDD Process. (Chapter - 2) 3.    Data Pre-processing : Data summarization, data cleaning, data integration and transformation, data reduction, data discretization and concept hierarchy generation, feature extraction , feature transformation, feature selection, introduction to Dimensionality Reduction, CUR decomposition. (Chapter - 3) 4.    Mining Frequent Patterns, Associations and Correlations : Efficient and scalable frequent item-set mining methods, mining various kind of association rules, from association mining to correlation analysis, Advanced Association Rule Techniques, Measuring the Quality of Rules. (Chapter - 4) 5.    Classification and Prediction : Classification vs. prediction, Issues regarding classification and prediction, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms, Neural Network-Based Algorithms, Rule-Based Algorithms, Combining Techniques, accuracy and error measures, evaluation of the accuracy of a classifier or predictor. Neural Network Prediction methods : Linear and nonlinear regression, Logistic Regression Introduction of tools such as DB Miner \/ WEKA \/ DTREG DM Tools. (Chapter - 5) 6.    Cluster Analysis : Clustering: Problem Definition, Clustering Overview, Evaluation of Clustering Algorithms, Partitioning Clustering -K-Means Algorithm, K-Means Additional issues, PAM Algorithm; Hierarchical Clustering – Agglomerative Methods and divisive methods, Basic Agglomerative Hierarchical Clustering, Strengths and Weakness; Outlier Detection, Clustering high dimensional data, clustering Graph and Network data. (Chapter - 6) 7.    Advance topics : Introduction to Web Mining, Spatial Data Mining, Temporal Mining, Text Mining and Multimedia Mining. (Chapter - 7)\u003c\/p\u003e","brand":"Technical Publications","offers":[{"title":"Default Title","offer_id":48277156298923,"sku":"13307710065","price":380.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0620\/3355\/9723\/files\/9789390770120_4_51f2731b-7147-4c1e-99f9-93d23ac8b24d.jpg?v=1792134976","url":"https:\/\/technicalpublications.in\/products\/9789390770120-5","provider":"Technical Publications","version":"1.0","type":"link"}