Association Rule Mining
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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.
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 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.
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.
Association Rule Mining Visualization
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Author : Bindu Madhavi K. Khambam
language : en
Publisher:
Release Date : 2015
Association Rule Mining Visualization written by Bindu Madhavi K. Khambam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Association rule mining algorithms are the fundamentals for a data mining course. Association rule mining helps to extract useful information from the data for various applications such as market analysis. Association rules are used for finding frequent items set, associations, correlations, or causal structures among sets of items or object. Generally, the students find it difficult to understand these key concepts because it requires abstract thinking. In addition, conveying a clear explanation of how these processes work is a bit of a challenge for the instructors too. Since the best way to understand complex algorithms is to see them in action, it would be very helpful if a visualization tool of these algorithms were available to the students to play with. Hence, the drive to come up with a data mining visualization tool that can animate a few of the most widely used and complex data mining algorithms. The objective of the project is to provide an association rule-mining tutorial and make the students understand the basic underlying concepts. Additionally the tutorial provides visualization tool of these algorithms, which will help the students to understand the complex algorithms better and they will be able to test them. This project is intended to create an exploration environment, in which students can learn through experimentation. It is targeted at the students wanting to practice algorithms that are being covered in class, as well as instructors wishing to embellish their lectures with an animated interface to help the students. An understanding of the underlying mechanics of algorithms is of great importance to students who are taking data mining courses.
Association Rule Mining
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Author : Chengqi Zhang
language : en
Publisher:
Release Date : 2014-01-15
Association Rule Mining written by Chengqi Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Association Rule Hiding For Data Mining
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Author : Aris Gkoulalas-Divanis
language : en
Publisher:
Release Date : 2010
Association Rule Hiding For Data Mining written by Aris Gkoulalas-Divanis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with 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 on data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the optimization problem of "hiding" sensitive association rules which due to its combinatorial nature admits a number of heuristic solutions that will be proposed and presented in this book. Exact solutions of increased time complexity that have been proposed recently are also presented as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a discussion regarding unsolved problems and future directions. 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.
Study Of Association Rule Mining And Different Hiding Techniques
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Author :
language : en
Publisher:
Release Date :
Study Of Association Rule Mining And Different Hiding Techniques written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. In this paper, we first focused on APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. We examined the data structure, implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. But a key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public, since among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. So, next we worked with some association rule hiding algorithms and examined their performances in order to analyze their time complexity and the impact that they have in the original database. We worked on two different side effects - one was the number of new rules generated during the hiding process and the other one was the number of non-sensitive rules lost during the process.
Data Mining For Association Rules And Sequential Patterns
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Author : Jean-Marc Adamo
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Data Mining For Association Rules And Sequential Patterns written by Jean-Marc Adamo 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.
Data mining includes a wide range of activities such as classification, clustering, similarity analysis, summarization, association rule and sequential pattern discovery, and so forth. The book focuses on the last two previously listed activities. It provides a unified presentation of algorithms for association rule and sequential pattern discovery. For both mining problems, the presentation relies on the lattice structure of the search space. All algorithms are built as processes running on this structure. Proving their properties takes advantage of the mathematical properties of the structure. Part of the motivation for writing this book was postgraduate teaching. One of the main intentions was to make the book a suitable support for the clear exposition of problems and algorithms as well as a sound base for further discussion and investigation. Since the book only assumes elementary mathematical knowledge in the domains of lattices, combinatorial optimization, probability calculus, and statistics, it is fit for use by undergraduate students as well. The algorithms are described in a C-like pseudo programming language. The computations are shown in great detail. This makes the book also fit for use by implementers: computer scientists in many domains as well as industry engineers.
Rare Association Rule Mining And Knowledge Discovery Technologies For Infrequent And Critical Event Detection
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Author : Koh, Yun Sing
language : en
Publisher: IGI Global
Release Date : 2009-08-31
Rare Association Rule Mining And Knowledge Discovery Technologies For Infrequent And Critical Event Detection written by Koh, Yun Sing and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-31 with Business & Economics categories.
"This book provides readers with an in-depth compendium of current issues, trends, and technologies in association rule mining"--Provided by publisher.