IDA-97 Call for Participation
Michael Berthold
berthold at ira.uka.de
Thu May 8 11:54:21 EDT 1997
CALL FOR PARTICIPATION
The Second International Symposium on Intelligent Data Analysis (IDA-97)
Birkbeck College, University of London
4th-6th August 1997
In Cooperation with
AAAI, ACM SIGART, BCS SGES, IEEE SMC, and SSAISB
[ http://web.dcs.bbk.ac.uk/ida97.html ]
You are invited to participate in IDA-97, to be held in the heart of London.
IDA-97 will be a single-track conference consisting of oral and poster
presentations, invited speakers, demonstrations and exhibitions. The
conference Call for Papers introduced a theme, "Reasoning About Data",
and many papers complement this theme, but other, exciting topics have emerged,
including exploratory data analysis, data quality, knowledge discovery and
data-analysis tools, as well as the perennial technologies of classification
and soft computing. A new and exciting theme involves analyzing time series
data from physical systems, such as medical instruments, environmental data
and industrial processes.
Information regarding registration can be found on the IDA-97 web page
(address listed above). Please note that there are reduced rates for early
registration (before 2nd June). Also there are still a limited number of
spaces available for exhibition, and potential exhibitors are encouraged to
book early (the application deadline is 2nd June).
Provisional Technical Program Schedule
Intelligent Data Analysis 97
Monday 4 August
10:00 to 10:30 Opening Ceremony
10:30 to 11:45 Invited Presentation I
Intelligent Data Analysis: Issues and Opportunities
.... David J Hand (UK)
(The abstract of Professor Hand's talk, and a brief biographical
sketch, can be found at the end of this document)
12:00 to 1:30PM LUNCH
1:30 to 2:45 PAPER SESSION 1: Exploratory Data Analysis and Preprocessing
Decomposition of heterogeneous classification problems
.... C Apte, S J Hong, J Hosking, J Lepre, E Pednault & B Rosen (USA)
Managing Dialogue in a Statistical Expert Assistant with a Cluster-based
User Model
.... M Muller (South Africa)
How to Find Big-Oh in Your Data Set (and How Not To)
.... C McGeoch, D Precup and P R Cohen (USA)
2:45 to 3:00 COFFEE BREAK
3:00 to 4:45 PAPER SESSION 2: Classification and Feature Selection
A Connectionist Approach to Structural Similarity Determination
as a Basis of Clustering, Classification and Feature Detection
.... K Schadler and F Wysotzki (Germany)
Efficient GA Bases Techniques for Automating the Design of
Classification Models
.... R Glover and P Sharpe (UK)
Data Representation and ML Techniques
.... C Lam, G West and T Caelli (Australia)
Development of a Knowledge-Driven Constructive Induction Mechanism
.... S Lo and A Famili (Canada)
4:45 to 5:00 COFFEE BREAK
5:00 to 5:45 POSTER SESSION I: Introduction
TOPIC ONE: Exploratory Data Analysis, Preprocessing and Tools
Data Classification Using a W.I.S.E. Toolbox
.... I Berry and P Gough (UK)
Mill's Methods for Complete Intelligent Data Analysis
.... T Cornish (UK)
Integrating Many Techniques for Discovering Structure in Data
.... D Gregory and P Cohen (USA)
Meta-Reasoning for Data Analysis Tool Allocation
.... R Levinson and J Wilkinson (USA)
Navigation for Data Analysis Systems
.... R St Amant (USA)
An Annotated Data Collection System to Support Intelligent Analysis
of Intensive Care Unit Data
.... C Tsien and J Fackler (USA)
A Combined Approach to Uncertain Data Manipulation
.... H Yu and A Ramer (Australia)
TOPIC TWO: Classification and Feature Selection
Oblique Linear Tree
.... J Gama (Portugal)
Feature selection for Neural Networks through Functional Links
found by Evolutionary Computation
.... S Haring, J Kok and M van Wezel (The Netherlands)
Overfitting Explained: a Case Study
.... D Jensen, T Oates and P Cohen (USA)
Exploiting Symbolic Learning in Visual Inspection
.... M Piccardi, R Cucchiara, M Bariani and P Mello (Italy)
Forming Categories in Exploratory Data Analysis and Data Mining
.... P Scott, H Williams and K Ho, (UK)
A systematic description of greedy optimisation algorithms for
cost sensitive generalisation
.... M van Someren, C Torres and F Verdenius (The Netherlands)
Automatic classification within object knowledge bases
.... P Valtchev and J Euzenat (France)
5:45 to 7:00 POSTER SESSION I: Posters
7:00 to 9:00 Conference Reception
Tuesday 5 August
9:15 to 10:30 Invited Presentation II
Given 3,000,000,000 Nucleotides, Induce a Person
or
Intelligent Data Analysis for Molecular Biology
.... Lawrence Hunter (USA)
(The abstract of Dr Hunter's talk, and a brief biographical
sketch, can be found at the end of this document)
10:30 to 10:45 COFFEE BREAK
10:45 to 12:00 PAPER SESSION 3: Medical Applications
ECG Segmentation using Time-Warping
.... H Vullings, M Verhaegen and H Vergruggen (The Netherlands)
Interpreting longitudinal data through temporal abstractions:
an application to diabetic patients monitoring
.... R Bellazzi and C Larizza (Italy)
Intelligent Support for Multidimensional Data Analysis in Environmental
Epidemiology
.... V Kamp and F Wietek (Germany)
12:00 to 1:30pm LUNCH
1:30 to 2:45 PAPER SESSION 4: Soft Computing
Network Performance Assessment for Neurofuzzy Data Modelling
.... S Gunn, M Brown and K Bossley (UK)
A Genetic Approach to Fuzzy Clustering with a Validity Measure Fitness
Function
.... S Nascimento and F Moura-Pires (Portugal)
The Analysis of Artificial Neural Network Data Models
....C Roadknight, D Palmer-Brown and G Mills (UK)
2:45 to 3:00 COFFEE BREAK
3:00 to 4:15 PAPER SESSION 5: Knowledge Discovery
A Strategy for Increasing the Efficiency of Rule Discovery in Data Mining
.... D McSherry (UK)
Knowledge-Based Concept Discovery from Textual Data
.... U Hahn and K Schnattinger (Germany)
Knowledge Discovery in Endgame Databases
.... M Schlosser (Germany)
4:15 to 4:30 COFFEE BREAK
4:30 to 5:15 POSTER SESSION II: Introduction
TOPIC ONE: Fuzzy and Soft Computing
Simulation Data Analysis Using Fuzzy Graphs
.... K-P Huber and M Berthold (Germany)
Mathematical Analysis of Fuzzy Classifiers
.... F Klawonn and E-P Klement (Germany; Austria)
Neuro-Fuzzy Diagnosis System with a Rated Diagnosis Reliability
and Visual Data Analysis
.... A Lapp and H Kranz (Germany)
Genetic Fuzzy Clustering by means of Discovering Membership Functions
.... M Turhan (Turkey)
TOPIC TWO: Data Mining
Parallelising Induction Algorithms for Data Mining
.... J Darlington, Y Guo, J Sutiwaraphun, H To (UK)
Data Analysis for Query Processing
.... J Robinson (UK)
Datum Discovery
.... L Siklossy and M Ayel (France)
Using neural network to extract knowledge from database
.... Y Zhou, Y Lu and C Shi (China)
TOPIC THREE: Estimation, Clustering
A Modulated parzen-Windows Approach for Probability Density Estimation
.... G van den Eijkel, J van der Lubbe and E Backer (The Netherlands)
Improvement on Estimating Quantiles in Finite Population Using
Indirect Methods of Estimation
.... M Garcia, E Rodriguez and A Cebrian (Spain)
Robustness of Clustering under Outliers
.... Y Kharin (Belarus)
The BANG-Clustering System: Grid-Based Data Analysis
.... E Schikuta and M Erhart (Austria)
TOPIC FOUR: Qualitative Models
Diagnosis of Tank Ballast Systems
.... B Schieffer and G Hotz (Germany)
Qualitative Uncertainty Models from Random Set Theory
.... O Wolkenhauer (UK)
5:15 to 6:30 POSTER SESSION II: Posters
7:30 - Conference Dinner
Wednesday 6 August
9:15 to 10:30 PAPER SESSION 6: Data Quality
Techniques for Dealing with Missing Values in Classification
.... W Liu, A White, S Thompson and M Bramer (UK)
The Use of Exogenous Knowledge to Learn Bayesian networks from
Incomplete Databases
.... M Ramoni and P Sebastiani (UK)
Reasoning about Outliers in Visual Field Data
.... J Wu, G Cheng and X Liu (UK)
10:30 to 10:45 COFFEE BREAK
10:45 to 12:00 PAPER SESSION 7: Qualitative Models
Reasoning about sensor data for automated system identification
.... E Bradley and M Easley (USA)
Modeling Discrete Event Sequences as State Transition Diagrams
.... A E Howe and G Somlo (USA)
Detecting and Describing Patterns in Time-Varying Data Using Wavelets
.... S Boyd (Australia)
12:00 to 12:30 Closing Ceremony
12:30 to 2:00pm LUNCH
2:00 to 4:00 IDA Open Business Meeting
--------------------------------------------------------------------------
Intelligent data analysis: issues and opportunities
David J. Hand
Modern data analysis is the product of the union of several disciplines:
statistics, computer science, pattern recognition, machine learning,
and others. Perhaps the oldest parent is statistics, being driven by the
demands of the different areas to which it has been applied. More recently,
however, the possibilities arising from powerful and available computers
have stimulated a revolution. Data of new kinds and in unimaginable
quantities now occur; they bring with them entirely new classes
of problems, problems to which the classical statistical solutions are not
always well-matched; these problems in turn require novel and original
solutions. In this talk I look at some of these new kinds of data, and
the associated problems and solutions. The data include data sets which
are large in dimensionality or number of records, data which are dependent
on each other, and that special kind of qualitative data
known as metadata. New problems arising from these data include
straightforward mechanical issues of how to handle them, how to estimate
descriptors and parameters (adaptive and sequential methods are obviously
more important than in classical statistics), the (ir)relevance of
significance tests, and automatic data analysis (as in
anomaly detection in large data sets or, quite differently, in automatic
model fitting). Some of the new types of model which are becoming so
important nicely illustrate the interdisciplinary nature of modern data
analysis: rule-based systems, hidden Markov models, neural networks,
genetic algorithms, and so on. These are briefly discussed.
All of this leads us to consider more carefully the link between data and
information and to recognise the complementary data analytic abilities and
powers of humans and computers. But we can go too far. If there is
'intelligent data analysis' there is also 'unintelligent data analysis'.
Two different manifestations of the latter are examined, and a cautionary
note sounded.
--------------
David J. Hand is Professor of Statistics at the Open University in the UK.
He has published over 150 papers and fourteen books, including Artificial
Intelligence Frontiers in Statistics, Practical Longitudinal Data Analysis,
and, most recently, Construction and Assessment of Classification Rules.
He is founding editor and editor-in-chief of the journal Statistics and
Computing. His research interests include developments at the interface
between statistics and computing, multivariate statistics, the foundations
of statistics, and applications in medicine, psychology, and finance.
------------------------------------------------------------------------
Given 3,000,000,000 Nucleotides, Induce a Person
or
Intelligent Data Analysis for Molecular Biology
Lawrence Hunter
In the last decade or so, large scale gene sequencing, combinatorial
biochemistry, DNA PCR and many other innovations in molecular biotechnology
have transformed biology from a data-poor science to a data-rich one. This
data is a harbinger of great change in medicine, in agriculture, and in our
fundamental understanding of life. However, the availability of an
exponentially growing onslaught of relevant data is only the first step
toward understanding. There are many scientifically and economically
significant opportunities (and challenges) for intelligent data analysis in
exploiting this information. In this talk, I will give a brief overview of
the kinds of data available and the open problems in the field, describe a
few successes, and speculate about the future.
-------------------
Dr. Lawrence Hunter is the director of the Machine Learning Project at the
(U.S.) National Library of Medicine, and a Fellow of the Krasnow Institute
of Advanced Study in Cognition at George Mason University. He received his
Ph.D. in Computer Science from Yale University in 1989. He edited the MIT
Press book "Artificial Intelligence and Molecular Biology," and was recently
elected the founding president of the International Society for
Computational Biology. His research contributions span the range from basic
contributions to machine learning methodology to development of IDA
technology for clinical and pharmaceutical industry applications.
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