Data mining and data warehousing : principles and practical techniques / Parteek Bhatia.

Format:

Book

Author/Creator:

Bhatia, Parteek, author.

Language:

English

Subjects (All):

Data mining--Textbooks.

Data mining.

Data warehousing--Textbooks.

Data warehousing.

Genre:

Textbooks.

Physical Description:

xxix, 477 pages ; 25 cm

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2019.

Summary:

"This textbook is written to cater to the needs of undergraduate students of computer science, engineering, and information technology for a course on data mining and data warehousing. It brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models, and NoSQL are discussed in detail. Unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding"-- Provided by publisher.

Contents:

Machine generated contents note: 1.1. Introduction to Machine Learning

1.2. Applications of Machine Learning

1.3. Defining Machine Learning

1.4. Classification of Machine Learning Algorithms

1.4.1. Supervised learning

1.4.2. Unsupervised learning

1.4.3. Supervised and unsupervised learning in real life scenario

1.4.4. Reinforcement learning

2.1. Introduction to Data Mining

2.2. Need of Data Mining

2.3. What Can Data Mining Do and Not Do?

2.4. Data Mining Applications

2.5. Data Mining Process

2.6. Data Mining Techniques

2.6.1. Predictive modeling

2.6.2. Database segmentation

2.6.3. Link analysis

2.6.4. Deviation detection

2.7. Difference between Data Mining and Machine Learning

3.1. About Weka

3.2. Installing Weka

3.3. Understanding Fisher's Iris Flower Dataset

3.4. Preparing the Dataset

3.5. Understanding ARFF (Attribute Relation File Format)

3.5.1. ARFF header section

3.5.2. ARFF data section

3.6. Working with a Dataset in Weka

3.6.1. Removing input/output attributes

3.6.2. Histogram

3.6.3. Attribute statistics

3.6.4. ARFF Viewer

3.6.5. Visualizer

3.7. Introduction to R

3.7.1. Features of R

3.7.2. Installing R

3.8. Variable Assignment and Output Printing in R

3.9. Data Types

3.10. Basic Operators in R

3.10.1. Arithmetic operators

3.10.2. Relational operators

3.10.3. Logical operators

3.10.4. Assignment operators

3.11. Installing Packages

3.12. Loading of Data

3.12.1. Working with the Iris dataset in R

4.1. Need for Data Preprocessing

4.2. Data Preprocessing Methods

4.2.1. Data cleaning

4.2.2. Data integration

4.2.3. Data transformation

4.2.4. Data reduction

5.1. Introduction to Classification

5.2. Types of Classification

5.2.1. Posteriori classification

5.2.2. Priori classification

5.3. Input and Output Attributes

5.4. Working of Classification

5.5. Guidelines for Size and Quality of the Training Dataset

5.6. Introduction to the Decision Tree Classifier

5.6.1. Building decision tree

5.6.2. Concept of information theory

5.6.3. Defining information in terms of probability

5.6.4. Information gain

5.6.5. Building a decision tree for the example dataset

5.6.6. Drawbacks of information gain theory

5.6.7. Split algorithm based on Gini Index

5.6.8. Building a decision tree with Gini Index

5.6.9. Advantages of the decision tree method

5.6.10. Disadvantages of the decision tree

5.7. Naive Bayes Method

5.7.1. Applying Naive Bayes classifier to the 'Whether Play' dataset

5.7.2. Working of Naive Bayes classifier using the Laplace Estimator

5.8. Understanding Metrics to Assess the Quality of Classifiers

5.8.1. The boy who cried wolf

5.8.2. True positive

5.8.3. True negative

5.8.4. False positive

5.8.5. False negative

5.8.6. Confusion matrix

5.8.7. Precision

5.8.8. Recall

5.8.9. F-Measure

6.1. Building a Decision Tree Classifier in Weka

6.1.1. Steps to take when applying the decision tree classifier on the Iris dataset in Weka

6.1.2. Understanding the confusion matrix

6.1.3. Understanding the decision tree

6.1.4. Reading decision tree rules

6.1.5. Interpreting results

6.1.6. Using rules for prediction

6.2. Applying Naive Bayes

6.3. Creating the Testing Dataset

6.4. Decision Tree Operation with R

6.5. Naive Bayes Operation using R

7.1. Introduction to Cluster Analysis

7.2. Applications of Cluster Analysis

7.3. Desired Features of Clustering

7.4. Distance Metrics

7.4.1. Euclidean distance

7.4.2. Manhattan distance

7.4.3. Chebyshev distance

7.5. Major Clustering Methods/Algorithms

7.6. Partitioning Clustering

7.6.1. k-means clustering

7.6.2. Starting values for the k-means algorithm

7.6.3. Issues with the k-means algorithm

7.6.4. Scaling and weighting

7.7. Hierarchical Clustering Algorithms (HCA)

7.7.1. Agglomerative clustering

7.7.2. Divisive clustering

7.7.3. Density-based clustering

7.7.4. DBSCAN algorithm

7.7.5. Strengths of DBSCAN algorithm

7.7.6. Weakness of DBSCAN algorithm

8.1. Introduction

8.2. Clustering Fisher's Iris Dataset with the Simple k-Means Algorithm

8.3. Handling Missing Values

8.4. Results Analysis after Applying Clustering

8.4.1. Identification of centroids for each cluster

8.4.2. Concept of within cluster sum of squared error

8.4.3. Identification of the optimum number of clusters using within cluster sum of squared error

8.5. Classification of Unlabeled Data

8.5.1. Adding clusters to dataset

8.5.2. Applying the classification algorithm by using added cluster attribute as class attribute

8.5.3. Pruning the decision tree

8.6. Clustering in R using Simple k-Means

8.6.1. Comparison of clustering results with the original dataset

8.6.2. Adding generated clusters to the original dataset

8.6.3. Apply J48 on the clustered dataset

9.1. Introduction to Association Rule Mining

9.2. Defining Association Rule Mining

9.3. Representations of Items for Association Mining

9.4. The Metrics to Evaluate the Strength of Association Rules

9.4.1. Support

9.4.2. Confidence

9.4.3. Lift

9.5. The Naive Algorithm for Finding Association Rules

9.5.1. Working of the Naive algorithm

9.5.2. Limitations of the Naive algorithm

9.5.3. Improved Naive algorithm to deal with larger datasets

9.6. Approaches for Transaction Database Storage

9.6.1. Simple transaction storage

9.6.2. Horizontal storage

9.6.3. Vertical representation

9.7. The Apriori Algorithm

9.7.1. About the inventors of Apriori

9.7.2. Working of the Apriori algorithm

9.8. Closed and Maximal Itemsets

9.9. The Apriori-TID Algorithm for Generating Association Mining Rules

9.10. Direct Hashing and Pruning (DHP)

9.11. Dynamic Itemset Counting (DIC)

9.12. Mining Frequent Patterns without Candidate Generation (FP Growth)

9.12.1. Advantages of the FP-tree approach

9.12.2. Further improvements of FP growth

10.1. Association Mining with Weka

10.2. Applying Predictive Apriori in Weka

10.3. Rules Generation Similar to Classifier Using Predictive Apriori

10.4. Comparison of Association Mining CAR Rules with J48 Classifier Rules

10.5. Applying the Apriori Algorithm in Weka

10.6. Applying the Apriori Algorithm in Weka on a Real World Dataset

10.7. Applying the Apriori Algorithm in Weka on a Real World Larger Dataset

10.8. Applying the Apriori Algorithm on a Numeric Dataset

10.9. Process of Performing Manual Discretization

10.10. Applying Association Mining in R

10.11. Implementing Apriori Algorithm

10.12. Generation of Rules Similar to Classifier

10.13. Comparison of Association Mining CAR Rules with J48 Classifier Rules

10.14. Application of Association Mining on Numeric Data in R

11.1. Introduction

11.2. Web Content Mining

11.2.1. Web document clustering

11.2.2. Suffix Tree Clustering (STC)

11.2.3. Resemblance and containment

11.2.4. Fingerprinting

11.3. Web Usage Mining

11.4. Web Structure Mining

11.4.1. Hyperlink Induced Topic Search (HITS) algorithm

11.5. Introduction to Modern Search Engines

11.6. Working of a Search Engine

11.6.1. Web crawler

11.6.2. Indexer

11.6.3. Query processor

11.7. PageRank Algorithm

11.8. Precision and Recall

12.1. The Need for an Operational Data Store (ODS)

12.2. Operational Data Store

12.2.1. Types of ODS

12.2.2. Architecture of ODS

12.2.3. Advantages of the ODS

12.3. Data Warehouse

12.3.1. Historical developments in data warehousing

12.3.2. Defining data warehousing

12.3.3. Data warehouse architecture

12.3.4. Benefits of data warehousing

12.4. Data Marts

12.5. Comparative Study of Data Warehouse with OLTP and ODS

12.5.1. Data warehouses versus OLTP: similarities and distinction

13.1. Introduction to Data Warehouse Schema

13.1.1. Dimension

13.1.2. Measure

13.1.3. Fact Table

13.1.4. Multi-dimensional view of data

13.2. Star Schema

13.3. Snowflake Schema

13.4. Fact Constellation Schema (Galaxy Schema)

13.5. Comparison among Star, Snowflake and Fact Constellation Schema

14.1. Introduction to Online Analytical Processing

14.1.1. Defining OLAP

14.1.2. OLAP applications

14.1.3. Features of OLAP

14.1.4. OLAP Benefits

14.1.5. Strengths of OLAP

14.1.6. Comparison between OLTP and OLAP

14.1.7. Differences between OLAP and data mining

14.2. Representation of Multi-dimensional Data

14.2.1. Data Cube

14.3. Implementing Multi-dimensional View of Data in Oracle

14.4. Improving efficiency of OLAP by pre-computing the queries

14.5. Types of OLAP Servers

14.5.1. Relational OLAP

14.5.2. MOLAP

14.5.3. Comparison of ROLAP and MOLAP

14.6. OLAP Operations

14.6.1. Roll-up

14.6.2. Drill-down

14.6.3. Slice and dice

14.6.4. Dice

14.6.5. Pivot

15.1. The Rise of Relational Databases

15.2. Major Issues with Relational Databases

15.3. Challenges from the Internet Boom

15.3.1. The rapid growth of unstructured data

15.3.2. Types of data in the era of the Internet boom

Contents note continued: 15.4. Emergence of Big Data due to the Internet Boom

15.5. Possible Solutions to Handle Huge Amount of Data

15.6. The Emergence of Technologies for Cluster Environment

15.7. Birth of NoSQI

15.8. Defining NoSQL from the Characteristics it Shares

15.9. Some Misconceptions about NoSQL

15.10. Data Models of NoSQI

15.10.1. Key-value data model

15.10.2. Column-family data model

15.10.3. Document data model

15.10.4. Graph databases

15.11. Consistency in a Distributed Environment

15.12. CAP Theorem

15.13. Future of NoSQL

15.14. Difference between NoSQL and Relational Data Models (RDBMS).

Notes:

Includes bibliographical references and index.

ISBN:

9781108727747

1108727743

OCLC:

1055456089

Publisher Number:

99987421676