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You will be able to find information about Data warehousing and data mining along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Data warehousing and data mining after reading this blog. Data warehousing and data mining has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Data warehousing and data mining can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Data warehousing and data mining are mentioned below in detail. If you are having a hard time understanding Data warehousing and data mining or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

Data warehousing and data mining Unit One

Data Warehousing

DataWarehousing,BusinessAnalysisandOn-LineAnalyticalProcessing(OLAP):Basic 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.

Data warehousing and data mining Unit Two

Data Mining

DataMining–Introduction: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.

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Data warehousing and data mining Unit Three

Data Mining

DataMining-FrequentPatternAnalysis: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 Patterns

Data warehousing and data mining Unit Four

Classification

Classification:Decision 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

Data warehousing and data mining Unit Five

Clustering Clustering Techniques

Clustering:ClusteringTechniques,Clusteranalysis,PartitioningMethods,Hierarchicalmethods,DensityBasedMethods,GridBasedMethods,Evaluationofclustering,Clusteringhighdimensionaldata,Clusteringwithconstraints,Outlieranalysis,outlierdetectionmethods.

Data warehousing and data mining Course Objectives

To understand data warehouse concepts, architecture, business analysisand tools To understand data pre-processing and data visualization techniques To study algorithms for finding hidden and interesting patterns in data To understand and apply various classification and clustering techniques using tools

Data warehousing and data mining Course Outcomes

At the end of the course, the students will be able to:Design a Data warehouse system and perform business analysis with OLAP toolsApply suitable pre-processing and visualization techniques for data analysis Apply frequent pattern and association rule mining techniques for data analysisApply appropriate classification techniques for data analysisApply appropriate clustering techniques for data analysis

Data warehousing and data mining Text Books

1)JiaweiHanandMichelineKamber,“DataMiningConceptsandTechniques”,ThirdEdition,Elsevier,201
2)Pang-NingTan,MichaelSteinbachandVipinKumar,IntroductiontoDataMining,Pearson,2016.

Data warehousing and data mining Reference Books

1)AlexBersonandStephenJ.Smith,―DataWarehousing,DataMining&OLAP‖,TataMcGraw–HillEdition,35thReprint2016.2)K.P.Soman,ShyamDiwakarandV.Ajay,―InsightintoDataMiningTheoryandPractice‖,EasternEconomyEdition,PrenticeHallofIndia,2006.3)IanH.WittenandEibeFrank,―DataMining:PracticalMachineLearningToolsandTechniques,Elsevier,SecondEdition.

1)https://www.saedsayad.com/data_mining_map.htm2)https://nptel.ac.in/courses/106/105/106105174/3)(NPTEL course by Prof.Pabitra Mitra) http://onlinecourses.nptel.ac.in/noc17_mg24/preview 4)(NPTEL course by Dr. Nandan Sudarshanam & Dr. Balaraman Ravindran) http://www.saedsayad.com/data_mining_map.htm

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Information about JNTU-K B.Tech R19 Data warehousing and data mining was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.

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