Here you can download the free Data Warehousing and Data Mining Notes pdf – DWDM notes pdf latest and Old materials with multiple file links to download. Data Warehousing and Data Mining Pdf Notes – DWDM Pdf Notes starts with the topics covering Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining, etc.
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Data Warehousing and Data Mining Pdf Notes – DWDM Pdf Notes
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Data Warehousing and Data Mining Pdf Notes – DWDM Pdf Notes
UNIT – I
Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining. Data Preprocessing : Needs Preprocessing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.
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UNIT – II
Data Warehouse and OLAP Technology for Data Mining Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation, Further Development of Data Cube Technology, From Data Warehousing to Data Mining. Data cube computation and Data Generalization: Efficient methods for Data cube computation, Further Development of Data Cube and OLAP Technology, Attribute Oriented Induction.
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UNIT – III
Mining Frequent Patterns, Associations And Correlations, Basic Concepts. Efficient And Scalable Frequent Itemset Mining Methods Mining Various Kinds Of Association Rules, From Associative Mining To Correlation Analysis, Constraint Based Association Mining.
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UNIT – IV
Classification and Prediction : Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Classification by Backpropagation, Support Vector Machines , Associative Classification, Lazy Learners , Other Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the accuracy of Classifier or a predictor, Ensemble methods.
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Data Warehousing and Data Mining Notes Pdf – DWDM Notes Pdf
UNIT – V
Cluster Analysis Introduction : Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods, Outlier Analysis.
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UNIT – VI
Mining Streams, Time Series and Sequence Data: Mining Data Streams Mining Time Series Data, Mining Sequence Patterns in Transactional Databases, Mining Sequence Patterns in biological Data, Graph Mining, Social Network Analysis and Multi Relational Data Mining
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UNIT – VII
Mining Object, Spatial , Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive mining of Complex Data objects, Spatial Data Mining, Multimedia Data Mining , Text Mining, Mining of the World WideWeb.
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UNIT – VIII
Applications and Trends In Data Mining : Data mining applications, Data Mining Products and Research Prototypes, Additional Themes on Data Mining and Social Impacts Of Data Mining.
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TEXT BOOKS :
- Data Mining – Concepts and Techniques – JIAWEI HAN & MICHELINE KAMBER Harcourt India.2nd ed 2006
- introduction to data mining- pang-ning tan, micheal steinbach and vipin kumar, pearson education.
REFERENCES:
- Data Mining Introductory and advanced topics –MARGARET H DUNHAM, PEARSON EDUCATION
- The Data Mining Techniques – ARUN K PUJARI, University Press.
- Data Warehousing in the Real World – SAM ANAHORY & DENNIS MURRAY. Pearson Edn Asia.
- DW – Data Warehousing Fundamentals – PAULRAJ PONNAIAH WILEY STUDENT EDITION.
- The Data Warehouse Life cycle Tool kit – RALPH KIMBALL WILEY STUDENT EDITION.
Note :- These notes are according to the r09 Syllabus book of JNTUH. In R13 ,8-units of R09 syllabus are combined into 5-units in r13 syllabus.Click here to check all the JNTU Syllabus books
Frequently asked questions
Q1: What is data mining?
A1: Extracting knowledge from large amount of information or data is called Data mining. Methods at the interaction of machine learning, artificial intelligence, data base system and statistics are involved in the computational process of discovering knowledge patterns in large set of data. Some of the key characteristics of data mining are,
- Likely outcome prediction
- Actionable information creation
- Large database and data set focus
- Automatic discovery of patterns in large data
Q2: What are the tasks of data mining?
A2:
- Clustering
- Anomaly detection
- Classification
- Association rule learning
- Regression
- Summarisation
Q3: What are the components of data mining?
A3: Data mining involves four major components. They are,
- Knowledge base
- Data mining engine
- Pattern evaluation module
- User Interface