Data Mining and Analysis: Fundamental Concepts and Algorithms

@inproceedings{Zaki2014DataMA,
  title={Data Mining and Analysis: Fundamental Concepts and Algorithms},
  author={Mohammed J. Zaki},
  year={2014}
}
The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts… Expand

Figures, Tables, and Topics from this paper

Paper Mentions

Pre-processing Methods of Data Mining
TLDR
Different data pre-processing techniques discussed in this paper could offer most suitable solutions for handling missing values and outliers in all kinds of large datasets such as electric load and weather datasets. Expand
Data Mining: The Textbook
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data miningExpand
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
TLDR
A real world problem presented in the ECDBL’2014 Big Data competition is used to provide a thorough analysis on the application of some preprocessing techniques, their combination and their performance. Expand
A Survey on Big Data, Mining: (Tools, Techniques, Applications and Notable Uses)
TLDR
This study investigates the most effective Big Data Mining techniques and their rationale applications in various social, medical and scientific fields. Expand
A Critical Survey of Data Grid Replication Strategies Based on Data Mining Techniques
TLDR
This is the first survey mainly dedicated to data grid replication strategies based on data mining techniques, and a new guideline to data mining application in the context of data Grid replication strategies is proposed. Expand
Data Mining: Mining Frequent Patterns, Associations Rules, and Correlations
TLDR
A survey of frequent pattern mining, a fundamental data mining task that deals with the search of recurring regularities in large data sets, and the most important classical algorithms to tackle this mining task are introduced. Expand
The Evolution of Data Mining Techniques to Big Data Analytics: An Extensive Study with Application to Renewable Energy Data Analytics
Recently big data have become a buzzword, which forced the researchers to expand the existing data mining techniques to cope with the evolved nature of data and to develop new analytic techniques.Expand
Big data preprocessing: methods and prospects
TLDR
The definition, characteristics, and categorization of data preprocessing approaches in big data are introduced and research challenges are discussed, with focus on developments on different big data framework, such as Hadoop, Spark and Flink. Expand
Big Data Technologies and Applications
TLDR
This book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students and can also be beneficial for business managers, entrepreneurs, and investors. Expand
MapDiff-FI : Map different sets for frequent itemsets mining
TLDR
Map Different Sets (MapDiff), a novel and more efficient itemset representation, is proposed, which can be reduce the size of datasets with keep all information of original data. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 137 REFERENCES
Data Mining: Concepts and Techniques
TLDR
This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data. Expand
Assessing data mining results via swap randomization
TLDR
For some datasets the structure discovered by the data mining algorithms is expected, given the row and column margins of the datasets, while for other datasets the discovered structure conveys information that is not captured by the margin counts. Expand
Data mining: practical machine learning tools and techniques, 3rd Edition
TLDR
This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Expand
Kernel Methods for Pattern Analysis
TLDR
This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so. Expand
On Clustering Validation Techniques
TLDR
The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated. Expand
SLIQ: A Fast Scalable Classifier for Data Mining
TLDR
Issues in building a scalable classifier are discussed and the design of SLIQ, a new classifier that uses a novel pre-sorting technique in the tree-growth phase to enable classification of disk-resident datasets is presented. Expand
Mining frequent patterns without candidate generation
TLDR
This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Expand
Efficient mining of frequent subgraphs in the presence of isomorphism
TLDR
This work proposes a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework it has developed to reduce the number of redundant candidates proposed. Expand
Data clustering: a review
TLDR
An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. Expand
Survey of clustering algorithms
  • R. Xu, D. Wunsch
  • Computer Science, Medicine
  • IEEE Transactions on Neural Networks
  • 2005
TLDR
Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated. Expand
...
1
2
3
4
5
...