 To enable learners of Engineering and Technology develop their basic communication skills in Multi – Core Architectures and Programming

Mobile Application Development LaboratoryĬommunication and Soft Skills - Laboratory Microprocessor and Microcontroller Laboratory Programming and Data Structure Laboratory II Transforms and Partial Differential Equations If you have any problem in downloading the above material, you can comment below.I TO VIII SEMESTER CURRICULUM AND SYLLABUS

Click below link to download Data Warehousing and Data Mining Syllabus Notes Question papers Question Banks 2 marks with answers Part B Questions with answers download.ĬS8075 Notes 1 link download - Click hereĬS8075 Notes 2 link download - Click hereĬS8075 Question Bank download - click hereĬS8075 Two Marks with Answers download - click here* Clustering Techniques – Cluster analysis-Partitioning Methods - Hierarchical Methods – Density Based Methods - Grid Based Methods – Evaluation of clustering – Clustering high dimensional data- Clustering with constraints, Outlier analysis-outlier detection methods.ĭatasets – Introduction, Iris plants database, Breast cancer database, Auto imports database - Introduction to WEKA, The Explorer – Getting started, Exploring the explorer, Learning algorithms, Clustering algorithms, Association–rule learners. Mining Frequent Patterns, Associations and Correlations – Mining Methods- Pattern Evaluation Method – Pattern Mining in Multilevel, Multi Dimensional Space – Constraint Based Frequent Pattern Mining, Classification using Frequent Patternsĭecision Tree Induction - Bayesian Classification – Rule Based Classification – Classification by Back Propagation – Support Vector Machines –– Lazy Learners – Model Evaluation and Selection-Techniques to improve Classification Accuracy. UNIT III DATA MINING - FREQUENT PATTERN ANALYSIS Introduction to Data Mining Systems – Knowledge Discovery Process – Data Mining Techniques – Issues – applications- Data Objects and attribute types, Statistical description of data, Data Preprocessing – Cleaning, Integration, Reduction, Transformation and discretization, Data Visualization, Data similarity and dissimilarity measures. UNIT I DATA WAREHOUSING, BUSINESS ANALYSIS AND ON-LINE ANALYTICAL PROCESSING (OLAP)īasic Concepts - Data Warehousing Components – Building a Data Warehouse – Database Architectures for Parallel Processing – Parallel DBMS Vendors - Multidimensional Data Model – Data Warehouse Schemas for Decision Support, Concept Hierarchies -Characteristics of OLAP Systems – Typical OLAP Operations, OLAP and OLTP.

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