A K PUJARI DATA MINING TECHNIQUES PDF

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Data Mining Techniques – Arun K. Pujari – Ebook download as PDF File .pdf), Text File .txt) or read book online. Arun K Pujari. Read “Data Mining Techniques” by Arun with Rakuten Kobo. Data Mining Techniques addresses all the major and latest techniques of data mining and. : Data Mining Techniques () by A. K. Pujari and a great selection of similar New, Used and Collectible Books available now at.

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Data Mining Techniques addresses all the major and latest techniques of data mining and data warehousing. It deals in detail with the latest algorithms for discovering association rules, decision trees, clustering, neural networks and genetic algorithms. Interesting and recent developments such as Support Vector Machines and Rough Set Theory are also covered in the book. The book also discusses the mining of web data, spatial data, temporal data and text data.

This book can serve as a textbook for students of daya science, mathematical science and management science. It can also be an excellent handbook for researchers in the area of data mining and data warehousing. The revised edition includes a comprehensive chapter on rough set theory.

The rough set theory, which is a tool of sets and relations for studying imprecision, vagueness, and uncertainty in data analysis, is a relatively new mathematical and artificial intelligence technique. The discussion on association rule mining has been extended to include rapid association rule mining RARMFP-Tree Growth Algorithm for discovering association rule and the Eclat and dEclat algorithms.

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These appear in Chapter 4. Practical Mininy Learning Tools and Techniques. Machine Learning with R. Machine Learning in Python.

Introduction to Information Retrieval. Python Machine Learning By Example. Machine Learning and Security. Machine Learning for Developers. Fundamentals of Stream Processing. The Text Mining Handbook. Professor Yanhong Annie Liu. Mastering Text Mining with R. Machine Learning for Data Streams.

Mastering Java Machine Learning. Handbook of Big Data Technologies. Advanced Machine Learning with Python. Artificial Intelligence for Big Data. The Functional Approach techniqeus Programming. Miining, Standards and Practices of Web Services. Database and Expert Systems Applications. Fundamentals of Predictive Text Mining. Deep Learning with Hadoop.

Readings in Artificial Intelligence and Software Engineering. Principles of Data Integration. Data Analysis for Network Cyber-Security. Clustering and Information Retrieval. Integration of Reusable Systems. Data Analysis with Open Source Tools. Schema Matching and Mapping.

Big Data Analytics with R and Hadoop. Big Data Analytics and Knowledge Discovery. Handbook of Constraint Programming. Redis Programming by Example. Advances in Databases and Information Systems. Automated Data Collection with R. Data Mining and Constraint Programming. Scalable Pattern Recognition Algorithms. Software Engineering and Methodology for Emerging Domains. Database Systems for Advanced Applications. Formal Aspects of Component Software.

A.K. Pujari (Author of Data Mining Techniques)

Applied Cryptography and Network Security. Model and Data Engineering. Machine Learning for Evolution Strategies. Distributed Computing and Internet Technology. Computational Intelligence in Data Mining. The Theory of Info-Dynamics: Rational Foundations of Information-Knowledge Dynamics.

Data Mining Techniques

Apache Spark Machine Learning Blueprints. Information and Communication Technology for Sustainable Development. Progress in Advanced Computing and Intelligent Engineering.

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