TAILIEUCHUNG - The top ten algorithms in data mining

In an effort to identify some of the most influential algorithms that have been widely used in the data mining community, the IEEE International Conference on Data Mining (ICDM, ∼icdm/) identified the top 10 algorithms in data mining for presentation at ICDM ’06 in Hong Kong. This book presents these top 10 data mining algorithms: , k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Na¨ıve Bayes, and CART. As the first step in the identification process, in September 2006 we invited theACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in. | Chapman Hall CRC Data Mining and Knowledge Discover Series CM U ri Igorithms in Data CRC Press A CHAPMAN b HALL BOOK The Top Ten Algorithms in Data Mining 2009 by Taylor Francis Group LLC Chapman Hall CRC Data Mining and Knowledge Discovery Series SERIES EDITOR Vipin Kumar University of Minnesota Department of Computer Science and Engineering Minneapolis Minnesota AIMS AND SCOPE This series aims to capture new developments and applications in data mining and knowledge discovery while summarizing the computational tools and techniques useful in data analysis. This series encourages the integration of mathematical statistical and computational methods and techniques through the publication of a broad range of textbooks reference works and handbooks. The inclusion of concrete examples and applications is highly encouraged. The scope of the series includes but is not limited to titles in the areas of data mining and knowledge discovery methods and applications modeling algorithms theory and foundations data and knowledge visualization data mining systems and tools and privacy and security issues. PUBLISHED TITLES UNDERSTANDING COMPLEX DATASETS Data Mining with Matrix Decompositions David skillicorn COMPUTATIONAL METHODS OE FEATURE SELECTION Huan Liu and Hiroshi Motoda CONSTRAINED CLUSTERING Advances in Algorithms Theory and Applications Sugato Basu Ian Davidson and Kiri L. Wagstaff KNOWLEDGE DISCOVERY FOR COUNTERTERRORISM AND LAW ENFORCEMENT David skillicorn MULTIMEDIA DATA MINING A Systematic Introduction to Concepts and Theory Zhongfei Zhang and Ruofei Zhang NEXT GENERATION OF DATA MINING Hillol Kargupta Jiawei Han Philip s. Yu Rajeev Motwani and Vipin Kumar DATA MINING FOR DESIGN AND MARKETING Yukio Ohsawa and Katsutoshi Yada GEOGRAPHIC DATA MINING AND KNOWLEDGE DISCOVERY Second Edition Harvey J. Miller and Jiawei Han THE TOP TEN ALGORITHMS IN DATA MINING Xindong Wu and Vipin Kumar 2009 by Taylor Francis Group .

• Cơ sở dữ liệu
• Data Mining
• Alternative Techniques
• training data
• Simple models
• Logistic regression
• Decision trees
• Data warehousing and data mining
• Data warehousing
• Data warehouse modelling
• Data mining functionalities
• Association rule mining
• FP growth algorithm
• K Nearest neighbor classification
• Lecture Data mining
• Bài giảng Khai thác dữ liệu
• Data mining for data streams
• Mining data streams
• Stream frequent pattern analysis
• Khai phá dữ liệu
• Overview of data mining
• Association rules
• Other Data Mining problems
• Applications of data mining
• Sequence data
• Sequential pattern mining
• Sequential pattern mining algorithms
• Data stream mining
• Data stream processing algorithms
• Cash register model
• Time series model
• Lecture Data mining and analysis
• Khai thác dữ liệu
• Data analysis foundations
• Frequent pattern mining
• Itemset mining
• Sequence mining
• Graph pattern mining
• Data warehouses
• On line analytical processing
• Association rules mining
• Mining association rules
• Computational complexity
• Frequent itemset generation
• Mining frequent closed patterns
• Data warehouse
• Data warehouse architecture
• Data warehouse implementation
• Data warehouse—subject oriented
• Graph data
• High dimensional data
• Numeric attributes
• Categorical attributes
• Kernel methods
• Dimensionality reduction
• Summarizing itemsets
• Rule assessment