Advances in Intelligent Data Analysis. Reasoning about Data

Second International Symposium, IDA-97, London, UK, August 4-6, 1997, Proceedings

Häftad, Engelska, 1997

Av Xiaohui Liu, Paul Cohen, Michael R. Berthold

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This book constitutes the refereed proceedings of the Second International Symposium on Intelligent Data Analysis, IDA-97, held in London, UK, in August 1997.The volume presents 50 revised full papers selected from a total of 107 submissions. Also included is a keynote, Intelligent Data Analysis: Issues and Opportunities, by David J. Hand. The papers are organized in sections on exploratory data analysis, preprocessing and tools; classification and feature selection; medical applications; soft computing; knowledge discovery and data mining; estimation and clustering; data quality; qualitative models.

Produktinformation

Utgivningsdatum1997-07-23
Mått155 x 235 x 35 mm
Vikt955 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor627
Upplaga1997
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540633464

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Artificiell intelligens inom Data och it
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Teknik: allmänt inom Naturvetenskap och teknik
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Systemvetenskap och AI inom Data och it

Intelligent data analysis: Issues and opportunities.- Decomposition of heterogeneous classification problems.- Managing dialogue in a statistical expert assistant with a cluster-based user model.- How to find big-oh in your data set (and how not to).- Data classification using a W.I.S.E. toolbox.- Mill's methods for complete Intelligent Data Analysis.- Integrating many techniques for discovering structure in data.- Meta-Reasoning for Data Analysis Tool Allocation.- Navigation for data analysis systems.- An annotated data collection system to support intelligent analysis of Intensive Care Unit data.- A combined approach to uncertain data analysis.- A connectionist approach to the distance-based analysis of relational data.- Efficient GA based techniques for automating the design of classification models.- Data representations and machine learning techniques.- Development of a knowledge-driven constructive induction mechanism.- Oblique linear tree.- Feature selection for neural networks through functional links found by evolutionary computation.- Building simple models: A case study with decision trees.- Exploiting symbolic learning in visual inspection.- Forming categories in exploratory data analysis and data mining.- A systematic description of greedy optimisation algorithms for cost sensitive generalisation.- Dissimilarity measure for collections of objects and values.- ECG segmentation using time-warping.- Interpreting longitudinal data through temporal abstractions: An application to diabetic patients monitoring.- Intelligent support for multidimensional data analysis in environmental epidemiology.- Network performance assessment for Neurofuzzy data modelling.- A genetic approach to fuzzy clustering with a validity measure fitness function.- The analysis of artificial neural network data models.- Simulation data analysis using Fuzzy Graphs.- Mathematical analysis of fuzzy classifiers.- Neuro-fuzzy diagnosis system with a rated diagnosis reliability and visual data analysis.- Genetic Fuzzy Clustering by means of discovering membership functions.- A strategy for increasing the efficiency of rule discovery in data mining.- Intelligent text analysis for dynamically maintaining and updating domain knowledge bases.- Knowledge discovery in endgame databases.- Parallel induction algorithms for data mining.- Data analysis for query processing.- Datum discovery.- A connectionist approach to extracting knowledge from databases.- A modulated Parzen-windows approach for probability density estimation.- Improvement on estimating quantites in finite population using indirect methods of estimation.- Robustness of clustering under outliers.- The BANG-clustering system: Grid-based data analysis.- Techniques for dealing with missing values in classification.- The use of exogenous knowledge to learn Bayesian Networks from incomplete databases.- Reasoning about outliers by modelling noisy data.- Reasoning about sensor data for automated system identification.- Modelling discrete event sequences as state transition diagrams.- Detecting and describing patterns in time-varying data using wavelets.- Diagnosis of tank ballast systems.- Qualitative uncertainty models from random set theory.