Knowledge extraction

About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.

Knowledge Discovery with Support Vector Machines

Abstract: An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

Privacy preserving association rule mining

Abstract: The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery that allow for the extraction of hidden knowledge in an enormous amount of data, impose new threats on the seamless integration of information. We consider the problem of building privacy preserving algorithms for one category of data mining techniques, association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to that of the original algorithms.

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Abstract: : Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) ISBN : #1466558210 | Date : 2013-08-21 Description : PDF-7caf7 | Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention t... Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Rough set methods and applications: new developments in knowledge discovery in information systems

Abstract: 1. Introduction.- Introducing the Book.- 1. A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on the Topic of the Book.- 2. Methods and Applications: Reducts, Similarity, Mereology.- 2. Rough Set Algorithms in Classification Problem.- 3. Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning.- 4. Knowledge Discovery by Application of Rough Set Models.- 5. Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey.- 3. Methods and Applications: Regular Pattern Extraction, Concurrency.- 6. Regularity Analysis and its Applications in Data Mining.- 7. Rough Set Methods for the Synthesis and Analysis of Concurrent Processes.- 4. Methods and Applications: Algebraic and Statistical Aspects, Conflicts, Incompleteness.- 8. Conflict Analysis.- 9. Logical and Algebraic Techniques for Rough Set Data Analysis.- 10. Statistical Techniques for Rough Set Data Analysis.- 11. Data Mining in Incomplete Information Systems from Rough Set Perspective.- 5. Afterword.- 12. Rough Sets and Rough Logic: A KDD Perspective.- Appendix: Selected Bibliofgraphy on Rough Sets.

Mining motifs in massive time series databases

Abstract: The problem of efficiently locating previously known patterns in a time series database (i.e., query by content) has received much attention and may now largely be regarded as a solved problem. However, from a knowledge discovery viewpoint, a more interesting problem is the enumeration of previously unknown, frequently occurring patterns. We call such patterns "motifs", because of their close analogy to their discrete counterparts in computation biology. An efficient motif discovery algorithm for time series would be useful as a tool for summarizing and visualizing massive time series databases. In addition it could be used as a subroutine in various other data mining tasks, including the discovery of association rules, clustering and classification. In this paper we carefully motivate, then introduce, a nontrivial definition of time series motifs. We propose an efficient algorithm to discover them, and we demonstrate the utility and efficiency of our approach on several real world datasets.