Association Rule Mining
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Frequent Pattern Mining
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2014-08-29
Frequent Pattern Mining written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-29 with Computers categories.
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Association Rule Mining
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Author : Chengqi Zhang
language : en
Publisher: Springer
Release Date : 2003-08-01
Association Rule Mining written by Chengqi Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-01 with Computers categories.
Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
Business Intelligence And Data Mining
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Author : Anil K. Maheshwari
language : en
Publisher:
Release Date : 2014-12-30
Business Intelligence And Data Mining written by Anil K. Maheshwari and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-30 with Business & Economics categories.
Data Analytics and Data-based Decision-making are hot topics now. Big Data has entered the common parlance. Many kinds of data are generated by business, social media, machines, and more. Organizations have a choice: they can be buried under the avalanche of data, or they can do something with it to increase competitive advantage. A new field of Data Science is born, and Data Scientist has been called the sexiest job of the decade. Students across a variety of academic departments, including business, computer science, statistics, and engineering are attracted to the idea of discovering new insights and ideas from data. This is a proposal for a short and lucid book on this whole area. It is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms, all within a single semester- or quarter-long course.
Lecture Notes In Data Mining
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Author : Michael W. Berry
language : en
Publisher: World Scientific
Release Date : 2006
Lecture Notes In Data Mining written by Michael W. Berry and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.
The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. This book is a series of seventeen edited OC student-authored lecturesOCO which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight. The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms. The book''s discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining. Sample Chapter(s). Chapter 1: Point Estimation Algorithms (397 KB). Contents: Point Estimation Algorithms; Applications of Bayes Theorem; Similarity Measures; Decision Trees; Genetic Algorithms; Classification: Distance Based Algorithms; Decision Tree-Based Algorithms; Covering (Rule-Based) Algorithms; Clustering: An Overview; Clustering Hierarchical Algorithms; Clustering Partitional Algorithms; Clustering: Large Databases; Clustering Categorical Attributes; Association Rules: An Overview; Association Rules: Parallel and Distributed Algorithms; Association Rules: Advanced Techniques and Measures; Spatial Mining: Techniques and Algorithms. Readership: An introductory data mining textbook or a technical data mining book for an upper level undergraduate or graduate level course."
Predictive Analytics Using Oracle Data Miner
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Author : Brendan Tierney
language : en
Publisher: McGraw Hill Professional
Release Date : 2014-08-08
Predictive Analytics Using Oracle Data Miner written by Brendan Tierney and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-08 with Computers categories.
Build Next-Generation In-Database Predictive Analytics Applications with Oracle Data Miner “If you have an Oracle Database and want to leverage that data to discover new insights, make predictions, and generate actionable insights, this book is a must read for you! In Predictive Analytics Using Oracle Data Miner: Develop & Use Oracle Data Mining Models in Oracle Data Miner, SQL & PL/SQL, Brendan Tierney, Oracle ACE Director and data mining expert, guides you through the basic concepts of data mining and offers step-by-step instructions for solving data-driven problems using SQL Developer’s Oracle Data Mining extension. Brendan takes it full circle by showing you how to deploy advanced analytical methodologies and predictive models immediately into enterprise-wide production environments using the in-database SQL and PL/SQL functionality. Definitely a must read for any Oracle data professional!” --Charlie Berger, Senior Director Product Management, Oracle Data Mining and Advanced Analytics Perform in-database data mining to unlock hidden insights in data. Written by an Oracle ACE Director, Predictive Analytics Using Oracle Data Miner shows you how to use this powerful tool to create and deploy advanced data mining models. Covering topics for the data scientist, Oracle developer, and Oracle database administrator, this Oracle Press guide shows you how to get started with Oracle Data Miner and build Oracle Data Miner models using SQL and PL/SQL packages. You'll get best practices for integrating your Oracle Data Miner models into applications to automate the discovery and distribution of business intelligence predictions throughout the enterprise. Install and configure Oracle Data Miner for Oracle Database 11g Release 11.2 and Oracle Database 12c Create Oracle Data Miner projects and workflows Prepare data for data mining Develop data mining models using association rule analysis, classification, clustering, regression, and anomaly detection Use data dictionary views and prepare your data using in-database transformations Build and use data mining models using SQL and PL/SQL packages Migrate your Oracle Data Miner models, integrate them into dashboards and applications, and run them in parallel Build transient data mining models with the Predictive Queries feature in Oracle Database 12c
Advances In Computing And Information Technology
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Author : Natarajan Meghanathan
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-08-13
Advances In Computing And Information Technology written by Natarajan Meghanathan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-13 with Computers categories.
The international conference on Advances in Computing and Information technology (ACITY 2012) provides an excellent international forum for both academics and professionals for sharing knowledge and results in theory, methodology and applications of Computer Science and Information Technology. The Second International Conference on Advances in Computing and Information technology (ACITY 2012), held in Chennai, India, during July 13-15, 2012, covered a number of topics in all major fields of Computer Science and Information Technology including: networking and communications, network security and applications, web and internet computing, ubiquitous computing, algorithms, bioinformatics, digital image processing and pattern recognition, artificial intelligence, soft computing and applications. Upon a strength review process, a number of high-quality, presenting not only innovative ideas but also a founded evaluation and a strong argumentation of the same, were selected and collected in the present proceedings, that is composed of three different volumes.
Compstat
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Author : Wolfgang Härdle
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Compstat written by Wolfgang Härdle and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
This COMPSTAT 2002 book contains the Keynote, Invited, and Full Contributed papers presented in Berlin, August 2002. A companion volume including Short Communications and Posters is published on CD. The COMPSTAT 2002 is the 15th conference in a serie of biannual conferences with the objective to present the latest developments in Computational Statistics and is taking place from August 24th to August 28th, 2002. Previous COMPSTATs were in Vienna (1974), Berlin (1976), Leiden (1978), Edinburgh (1980), Toulouse (1982), Pra~ue (1984), Rome (1986), Copenhagen (1988), Dubrovnik (1990), Neuchatel (1992), Vienna (1994), Barcelona (1996), Bris tol (1998) and Utrecht (2000). COMPSTAT 2002 is organised by CASE, Center of Applied Statistics and Eco nomics at Humboldt-Universitat zu Berlin in cooperation with F'reie Universitat Berlin and University of Potsdam. The topics of COMPSTAT include methodological applications, innovative soft ware and mathematical developments, especially in the following fields: statistical risk management, multivariate and robust analysis, Markov Chain Monte Carlo Methods, statistics of E-commerce, new strategies in teaching (Multimedia, In ternet), computerbased sampling/questionnaires, analysis of large databases (with emphasis on computing in memory), graphical tools for data analysis, classification and clustering, new statistical software and historical development of software.
Association Rule Hiding For Data Mining
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Author : Aris Gkoulalas-Divanis
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-05-17
Association Rule Hiding For Data Mining written by Aris Gkoulalas-Divanis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-17 with Computers categories.
Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.
Data Mining
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Author : Dr. Suneel Pappala
language : en
Publisher: Blue Rose Publishers
Release Date : 2022-08-25
Data Mining written by Dr. Suneel Pappala and has been published by Blue Rose Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-25 with Computers categories.
DATA MINING IS USE TO COMPUTER SCIENCE AND ENGINEERING AND INFORMATION TECHNOLOGY STUDENTS. Data Mining is The process of automatically discovering useful information in large data repositories. – Observation = case, record, instance – Variable = field, attribute – Analysis of dependence vs interdependence = Supervised vs unsupervised learning – Relationship = association, concept – Dependent variable Data Mining is mainly concentrated on Association rule, Mining Frequent Patterns it is concentrated on Associations and correlations and also concentrated on Mining Methods,Mining Various kinds of Association Rules,Correlation Analysis, Constraint based Association mining. Graph Pattern Mining SPM. Classification and Prediction ,Basic concepts,Decision tree induction,Bayesian classification, Rule–based classification, Lazy learner. Cluster analysis,Types of Data in Cluster Analysis,Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical Methods,Density Based Methods, Grid Based Methods, Outlier Analysis. Basic concepts in Mining data streams Mining Time series data Mining sequence patterns in Transactional databases Mining Object Spatial Multimedia Text and Web data Spatial Data mining Multimedia Data mining Text Mining Mining the World Wide Web.
Association Rule Mining Using Vertical Apriori
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Author : Bassel H. Dhaini
language : en
Publisher:
Release Date : 2004
Association Rule Mining Using Vertical Apriori written by Bassel H. Dhaini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Data mining categories.
The aim of data mining as a scientific research is developing methods to analyze large amounts of data in order to discover interesting regularities or exceptions. Typical problems, which should be resolved during developing effective data mining algorithms, arise from the large sizes of both: The data sets used in the data mining process and the patterns results sets (for example in rules) which form discovered knowledge. Scientific researchers are oriented to find the most advantageous (i.e. most effective) solutions both during the data preparation stage and exploration and finally post- processing to obtain results. During mining of association rules, the main effort has been put so far in developing more and more sophisticated mining algorithms finding interesting patterns in the appropriately prepared data. One problem that still needs to be tackled is the problem of excessive Database scans. Most of Association rules algorithms are extensions or derivatives of the Apriori algorithm, so mostly all of them use the technique of scanning the Database many times in order to obtain the association rules, this process (lot of Database Scans) is very time consuming. In this thesis we develop an optimization of the Apriori algorithm namely Vertical Apriori, using the C++ bitset data structure (an optimized version of bit vectors). Performance improvements will be demonstrated through our experiments section in chapter 6.