Data mining III
著者
書誌事項
Data mining III
（Management information systems, v. 6）
WIT Press, c2002
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注記
Proceedings of the Third International Conference on Data Mining held in 2002 at Bologna
Includes bibliographical references and index
内容説明・目次
内容説明
Data mining brings together techniques from machine learning, pattern recognition, statistics, databases, linguistics and visualization in order to extract information from large databases. Originally principally concerned with behavioural applications, such as the understanding of customer behaviour, its scope has now been widened with the introduction of Text Mining techniques. Areas now encompassed by data mining include military, market, and competitive intelligence applications, taxonomies and internet search techniques, and knowledge management applications. Featuring almost 100 contributions from academics and practitioners working around the world, this book contains the proceedings of the Third International Conference on Data Mining.The following topics are covered: data mining and technologies; knowledge discovery; text mining, structure mining and context mining; data analysis and data mining on large databases; data warehousing and databases; decision trees; neural networks; tools for pattern discovery; clustering and classification techniques; applications in business and industry; applications in health and medicine; applications in science and engineering; visualisation in data mining; web clickstream analysis; and web mining.
目次
- Section 1 Data mining and technologies: Data mining and soft computing
- Hard hats for data miners: myths and pitfalls of data mining
- A method for knowledge representation and discovery based on composing and manipulating logical equations
- Comparing dissimilarity measures for probabilistic symbolic objects
- A data mining toolset for distributed high-performance platforms
- An integrated platform for spatial data mining and interactive visual analysis
- Combining data mining and optimization for campaign management
- Implementing data mining algorithms with Microsoft(r) SQL Server
- A study of re-sampling methods with regression modelling
- Mining itemsets - an approach to longitudinal and incremental association rule mining
- Generating clusters' explanations in just one data scan
- An alternative method for extracting unexpected patterns from huge attributes using conditional contingency table in marketing
- A statistical model to predict gift patterns in large distribution (GDO), industry and trade promotions
- Outlier detection and data association for data mining criminal incidents. Section 2 Knowledge discovery: Knowledge discovery and supervised machine learning in a construction project database
- Visualizing interestingness
- SPADA: A spatial association discovery system
- A comparative study of two knowledge discovery tools: barchart versus scatterplot
- The inventive power of Learning Classifier Systems: a contribution to data mining
- Stroke risk factors classification modelling
- A propositional satisfiability approach in mining compact rules
- Rule association based alarm correlation in telecommunication management network (TMN)
- Knowledge discovery in textile field - analysis of a cotton fibre properties database
- Building an online purchasing behavior analytical system with neural network
- On high dimensional data spaces
- Process modelling analysis: comparison between activity-oriented models, product-oriented models and decision-oriented models. Section 3 Text mining, structure mining and context mining: Feature selection using support vector machines
- An evaluation component for categorization systems
- Integration of text and data mining
- Opinion classification through information extraction
- Textual data mining by parsing
- NL-OOPS: a requirements analysis tool based on natural language processing
- "NIBA - TAG" - A tool for analyzing and preparing German texts
- Mining the web to validate answers to natural language questions
- Text mining: crossing the chasm between the academy and the industry
- Study of category score algorithms for k - NN classifier. Section 4 Data analysis and data mining on large databases: Time series data analysis and pre-process on large databases
- Towards scaling up induction of second-order decision tables
- The window algorithm. (part contents)
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