- Data Mining and Data Warehousing
- Sequence Analysis and Data Mining
- Statistical Techniques in Data Mining
1. Data Mining and Data Warehousing
|1."Data Mining: Concepts and Techniques" by Han|
“Data Mining: Concepts and Techniques” Book Review: This book provides an understanding and application of the theory and practice of discovering patterns hidden in large data sets. It also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. It addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases etc. The book provides a comprehensive and practical understanding of the concepts and techniques.The book is useful for anyone interested in learning powerful data mining techniques to meet real business challenges.
|2."Data Warehousing" by Reema Thareja|
“Data Warehousing” Book Review: This book provides a thorough understanding of the fundamentals of data warehousing. The readers also get knowledge for creating and managing a data warehouse. The book introduces the various features and architecture of a data warehouse followed by a detailed study of the business requirements and dimensional modelling. It goes on to discuss the components of a data warehouse and then provides a thorough understanding of the building and maintenance of a data warehouse. The book includes several examples to illustrate concepts, contains several review questions and other end-chapter exercises to test the understanding of students. The book is designed for the students of Computer Science & Engineering (BE/Btech), computer applications (BCA/MCA) and computer science (B.Sc).
|3."Data Warehousing and Data Mining" by Singh M|
“Data Warehousing and Data Mining” Book Review: This book covers the basic concepts of data mining and warehousing. It also includes many case studies from experts in the field that provide a practical understanding of the subject matter to the students. The detailed explanations on the practical side with software tools such as Oracle BI, Weka and R are also included in the book. All the latest research trends in data mining such as ensemble learning, Web mining, bioinformatics and data warehousing with Oracle BI are discussed in detail. In addition it also discusses spatial data mining, big data, cloud computing and CRM. The book is useful for graduate and undergraduate students of computer science.
|4."Data Mining and Warehousing" by S Prabhu|
“Data Mining and Warehousing” Book Review: This book provides a systematic introduction to the principles of Data Mining and Data Warehousing. It covers the entire range of data mining algorithms (prediction, classification and association), data mining products and applications, stages of implementing processes in detail. It gives an overall idea about ETL tools, Software Products, Schemas, Partition, Back up, Recovery and Tuning. It should be useful to undergraduate and postgraduate students of Computer Science and Information Technology. It will be useful for practitioners and research scholars.
|5."Data Mining and Warehousing" by Khushboo and Sandeep|
“Data Mining and Warehousing” Book Review: This book provides a comprehensive compilation of research available in this emerging and increasingly important field. The book offers tools, designs and outcomes of the utilization of data mining and warehousing technologies, such as algorithms, concept lattices, multidimensional data and online analytical processing.It provides an overview of available approaches, techniques, open problems and applications related to data warehousing and mining. The book is designed for the students of Computer Science & Engineering (BE/Btech), computer applications (BCA/MCA) and computer science (B.Sc).
|6."Introducing Data Science: Big Data, Machine Learning, and more, using Python tools" by Davy Cielen and Arno Meysman|
“Introducing Data Science: Big Data, Machine Learning, and more, using Python tools” Book Review: This book explains important data science concepts and teaches the readers how to accomplish the fundamental tasks that occupy data scientists. It describes important topics such as data visualization, graph databases, the use of NoSQL and the data science process. The readers will learn how Python allows to gain insights from big data sets that need to be stored on multiple machines. The book is useful for undergraduate and postgraduate students of Computer Science and Information Technology.
|7."Data Warehousing: OLAP and Data Mining" by Nagabhushana S|
“Data Warehousing: OLAP and Data Mining” Book Review: This book describes planning, managing, designing, implementing, supporting, maintaining and analyzing data warehouses in organizations. It also provides various mining techniques as well as issues in practical use of Data Mining Tools. The book is designed for IT students and professionals to learn or implement data warehousing technologies. It will also benefit the specialists, trainers and IT users.
|8."Data Mining and Warehousing" by M Sudheep Elayidom|
“Data Mining and Warehousing” Book Review: This book covers the basic concepts of data mining and warehousing. It also includes many case studies from experts in the field that provide a practical understanding of the subject matter to the students. The detailed explanations on the practical side with software tools such as Oracle BI, Weka and R are also included in the book. All the latest research trends in data mining such as ensemble learning, Web mining, bioinformatics and data warehousing with Oracle BI are discussed in detail. In addition it also discusses spatial data mining, big data, cloud computing and CRM. The book is useful for graduate and undergraduate students of computer science.
|9."Python for Data Science for Dummies" by John Paul Mueller and Luca Massaron|
“Python for Data Science for Dummies” Book Review: This book teaches the advantage of Python programming to acquire, organize, process, and analyze large amounts of information to identify trends and patterns. It also describes how to apply basic concepts in the field of Python. It covers the fundamentals of Python data analysis programming and statistics. The book also focuses on important data science concepts like probability, random distributions, hypothesis testing and regression models. The readers will be able to apply statistical concepts such as probability and random distributions. The book is useful for data scientists as well as students who wish to learn Python and its applications.
|10."The Encyclopedia of Data Warehousing and Mining" by John Wang|
“The Encyclopedia of Data Warehousing and Mining” Book Review: This book is a comprehensive study of data warehousing and mining. It provides a thorough examination of the issues of importance in the rapidly changing field of data warehousing and mining. It gives an overall idea about ETL tools, Software Products, Schemas, Partition, Back up, Recovery and Tuning. It should be useful to undergraduate and postgraduate students of Computer Science and Information Technology. It will be useful for practitioners and research scholars.
|11."DATA MINING AND ANALYSIS:FUNDAMENTAL CONCEPTS AND ALGORITHMS" by MOHAMMED J ZAKI/ WAGNER MEIRA Jr|
“DATA MINING AND ANALYSIS:FUNDAMENTAL CONCEPTS AND ALGORITHMS” Book Review: This book presents algorithms related to data mining and analysis from the basics. It describes methods for analyzing patterns and models of all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. The book serves as a textbook for undergraduate and graduate students in the field of data mining. Further the book discusses exploratory data analysis, pattern mining, clustering and its classification. In addition, it contains abundant examples with algorithmic perspective.
|12."Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining)" by Daniel T Larose and Chantal D Larose|
“Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining)” Book Review: This book is for Data Mining and Predictive Analytics, computer science and statistics, MBA students and for the chief executive students. This book has association rules, clustering, neural networks, logistic regression and multivariate analysis. It has hands-on analysis problems. This book has problems and their solutions from large, real-world data sets.
|13."Advances in Machine Learning and Data Mining for Astronomy (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)" by Michael J Way|
Advances in Machine Learning and Data Mining for Astronomy (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)” by Michael J Way Book Review: The useful moves toward support in this book have prompted expanding the quality of numerous effective items through giving a superior comprehension of purchaser needs, current item and interaction execution and an ideal future state. In 2009, Forthcoming Rossi and Viktor Mirtchev brought their functional factual speculation forward and made the course “Insights for Food Researchers” . The plan of the course was to help item and interaction engineers increment the likelihood of their undertaking’s prosperity through the joining of functional measurable deduction in their difficulties. The course has since developed and has become the premise of this book. Presents itemized depictions of factual ideas and generally utilized measurable devices to all the more likely investigate information and decipher resultsDemonstrates exhaustive models and explicit functional issues of what food researchers face in their work and how the instruments of measurements can assist them with making more educated decisionsProvides data to show how measurable devices are applied to improve research results, upgrade item quality, and advance by and large item advancement.
|14."Optimization Based Data Mining" by Yingjie Tian|
“Optimization Based Data Mining” Book Review: The book is an excellent blend of theoretical as well as practical concepts of data mining. The chapters of this book are majorly based on support vector machines and multiple criteria programming. To give better practical knowledge and relatable content to the readers many case studies and real-life applications of data mining are highlighted in this textbook. The book presents highlights available scope for further research and gives knowledge about many developments that are due in this field. The book will be an asset for the practitioners and graduates related to data mining, bioinformatics, and petroleum engineering.
|15."Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)" by Charu C Aggarwal and Chandan K Reddy|
“Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)” Book Review: This book covers from basic to more complex topics on data clustering approaches. The book focuses on 3 primary aspects of data clustering namely-Methods, Domains and Variations and Insights. In Methods, topics such as feature selection, agglomerative, partitional,density- based , probabilistic clustering and many more are discussed. In the second aspect, concepts like categorical, text, multimedia, graph, biological, stream, etc. data are discussed. In the third and the last aspect, topics like semi supervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation are discussed. The book also explains various techniques to verify the quality of clusters with the help of supervision, human intervention and the automated generation of alternative clusters. This book is mainly for research in the field of Data Clustering Algorithms and Applications.
|16."Pocket Data Mining: Big Data on Small Devices (Studies in Big Data)" by Mohamed Medhat Gaber and Frederic Stahl|
“Pocket Data Mining: Big Data on Small Devices (Studies in Big Data)” Book Review: This book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. It also provides details of techniques used and thorough experimental studies. It also provides a detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed in this book.
|17."Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)" by Alexander Paprotny and Michael Thess|
“Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)” Book Review: This book presents promising results of numerous experiments on real-world data. The area of real time data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today’s “classic” data mining systems. This book provides a general introduction to methods of real time analytics and sets out their advantages and disadvantages as compared with conventional analytics methods, which learn only from historical data. In particular, we stress the difficulties in the development of theoretically sound real time analytics methods. It also includes application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.
|18."Data Mining: Uses in Commercial Applications" by Katiyar Vinodani|
“Data Mining: Uses in Commercial Applications” Book Review: This book shows the data mining techniques to extract the value of data but only few practitioners have used neural networks in data mining though this method has proven successful in many situations. The topics included in this book are: (1) implementation of neural networks in analysis of marketing variables; (2) sensitivity analysis and variable reduction through weight analysis; and (3) showing how, by inclusion of Unknown, better results can be obtained from neural networks.Neural network research is now being driven by industry, as more business problems are attempted and new research challenges emerge.
|19."Data Mining and Business Analytics with R" by Johannes Ledolter|
“Data Mining and Business Analytics with R” Book Review: This book provides the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. It also contains a thorough discussion and extensive demonstration of the theory behind the most useful data mining tools. It also provides illustrations of how to use the outlined concepts in real-world situations and readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials. It also has numerous exercises to help readers with computing skills and deepen their understanding of the material. It is useful for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
2. Sequence Analysis and Data Mining
|1."Data Mining Techniques for Protein Sequence Analysis" by Duraisamy Ramyachitra and Pandurangan Manikandan|
“Data Mining Techniques for Protein Sequence Analysis” Book Review: In this book are discussed principles of bioinformatics, datamining, evolutionary computing and its mutual intersection. Data-mining by means of selected evolutionary techniques are discussed with attention on protein structure, segmentation and state of art in the field of evolutionary algorithms use. Basic principles and terminology of evolutionary algorithms, as well as two evolutionary algorithms are mentioned here – differential evolution and self-organizing migrating algorithm.
|2."Sequence Data Mining (Advances in Database Systems)" by Guozhu Dong and Jian Pei|
“Sequence Data Mining (Advances in Database Systems)” Book Review: This book details both frequent/closed sequence patterns and the similarity sequence patterns and motifs. Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.
|3."Problems and Solutions in Biological Sequence Analysis" by Mark Borodovsky and Svetlana Ekisheva|
“Problems and Solutions in Biological Sequence Analysis” Book Review: This book provides a large collection of bioinformatics problems with accompanying solutions. Notably, the problem set includes all of the problems offered in Biological Sequence Analysis, by Durbin et al. (Cambridge, 1998), widely adopted as a required text for bioinformatics courses at leading universities worldwide. Although many of the problems included in Biological Sequence Analysis as exercises for its readers have been repeatedly used for homework and tests, no detailed solutions for the problems were available. Bioinformatics instructors had therefore frequently expressed a need for fully worked solutions and a larger set of problems for use on courses. This book provides just that: following the same structure as Biological Sequence Analysis and significantly extending the set of workable problems, it will facilitate a better understanding of the contents of the chapters in BSA and will help its readers develop problem-solving skills that are vitally important for conducting successful research in the growing field of bioinformatics.
|4."Advances in Knowledge Discovery and Data Mining" by Joshua Zhexue Huang and Longbing Cao|
“Advances in Knowledge Discovery and Data Mining” Book Review: This book constitutes the refereed proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, held in Nanjing, China, May 2007. It covers new ideas, original research results and practical development experiences from all KDD-related areas including data mining, machine learning, data warehousing, data visualization, automatic scientific discovery, knowledge acquisition and knowledge-based systems.
|5."Advances in Sequence Analysis: Theory, Method, Applications (Life Course Research and Social Policies)" by Philippe Blanchard and Felix Bühlmann|
“Advances in Sequence Analysis: Theory, Method, Applications (Life Course Research and Social Policies)” Book Review: This book includes innovative contributions on life course studies, transitions into and out of employment, contemporaneous and historical careers, and political trajectories. The approach presented in this book is now central to the life-course perspective and the study of social processes more generally. This volume promotes the dialogue between approaches to sequence analysis that developed separately, within traditions contrasted in space and disciplines. It includes the latest developments in sequential concepts, coding, atypical datasets and time patterns, optimal matching and alternative algorithms, survey optimization, and visualization. Overall the book reassesses the classical uses of sequences and it promotes new ways of collecting, formatting, representing and processing them. The introduction provides basic sequential concepts and tools, as well as a history of the method. Chapters are presented in a way that is both accessible to the beginner and informative to the expert.
|6."Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)" by Jiawei Han|
“Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)” Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)” Book Review: This book introduces the fundamentals of data mining and explores latest tools and techniques. It explains basic data mining concepts like OLAP, concept description, data preprocessing, classification and prediction, association rules and cluster analysis. It then presents advanced data mining techniques like extracting information from varied and complex sources other than just relational databases. This includes multimedia databases, object databases, time-series databases and spatial databases. It also looks at harvesting data from varied sources on the world wide web and extracting useful information from it. The book is useful for graduate and undergraduate students of computer science.
3. Statistical Techniques in Data Mining
|1."Principles of Data Mining" by D J Hand and P Smith|
“Principles of Data Mining” Book Review: This book covers the core principles of data mining. main topics introduced measurement in data, visualising exploring data, data analysis and uncertainty. Other topics included are A systematic overview of data mining algorithms, models and patterns, search and optimization methods, descriptive modelling. List of tables and list of figures are provided at the start of the book. All the algorithms and programs are discussed in detail. This book is useful for undergraduate computer science and information technology students.
|2."The Elements of Statistical Learning: Data Mining, Inference and Prediction" by T Hastie and J H Friedman|
“The Elements of Statistical Learning: Data Mining, Inference and Prediction” Book Review: This book includes the main elements of statistical learning. main topics namely overview of supervised learning, linear methods for regression, linear methods for classification are introduced. Other topics mentioned are basic expansions and regularisation kernel methods, model assessment and selection. Exercises are also added at the end of each chapter for student’s practice. This book is useful for computer engineering students.
|3."Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions" by Nuno M.M. Ramos and João M.P.Q. Delgado|
“Application of Data Mining Techniques in the Analysis of Indoor Hygrothermal Conditions” Book Review: This book discusses data mining techniques used in the analysis of indoor hygrothermal conditions. Main topics introduced are indoor hygrothermal conditions, descriptive statistics, multiple regression analysis, classification trees. Other topics included are Case studies on outdoor conditions, in temperature, indoor relative humidity and applications of data mining techniques. All the techniques and methods are described with proper programs. This book is suitable for advanced computer science engineering students.
|4."Mastering Data Mining-The Art and Science of CRM" by M J A Berry and G S Linoff|
Book Review: This book offers a case study to best practices in the field of commercial data mining. The book also presents data mining tools and techniques thereby helping in making better business decisions. The book also shifts the focus from understanding data mining techniques to achieving business results thereby stressing on customer relationship management. The book also applies data mining techniques to solve many practical business problems. The book also explains the formulation of business problem, data analysis, result evaluation and many more.
|5."Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)" by James Wu and Stephen Coggeshall|
“Foundations of Predictive Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)” Book Review: It explains the fundamentals to analyse data and building models for practical applications. It deals with statistical and linear algebra foundation of modelling methods. It discusses the copula functions, cornish-fisher expansion and other statistical techniques. Linear and non-linear methods are covered in this book. It also describes the methods used in time series and forecasting such as ARIMA, GARCH and survival analysis. This book includes various techniques for data analysis and modelling.