Advances in intelligent data analysis reasoning about data : second International Symposium, IDA-97 London, UK, August 4-6, 1997 : proceedings
Author(s)
Bibliographic Information
Advances in intelligent data analysis reasoning about data : second International Symposium, IDA-97 London, UK, August 4-6, 1997 : proceedings
（Lecture notes in computer science, 1280）
Springer, c1997
Available at / 47 libraries
-
Library, Research Institute for Mathematical Sciences, Kyoto University数研
L/N||LNCS||128097028165
-
INTERNATIONAL CHRISTIAN UNIVERSITY LIBRARY図
V.1280007.6/L507/v.128005006067,
007.6/L507/v.128005006067 -
University of Tsukuba Library, Library on Library and Information Science
007.08:L-49:1280971005690
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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.
by "Nielsen BookData"