1: State-of-the-Art in Research.- Generality Is Predictive of Prediction Accuracy.- Visualisation and Exploration of Scientific Data Using Graphs.- A Case-Based Data Mining Platform.- Consolidated Trees: An Analysis of Structural Convergence.- K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results.- Efficiently Identifying Exploratory Rules' Significance.- Mining Value-Based Item Packages - An Integer Programming Approach.- Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System.- Use of Data Mining in System Development Life Cycle.- Mining MOUCLAS Patterns and Jumping MOUCLAS Patterns to Construct Classifiers.- A Probabilistic Geocoding System Utilising a Parcel Based Address File.- Decision Models for Record Linkage.- Intelligent Document Filter for the Internet.- Informing the Curious Negotiator: Automatic News Extraction from the Internet.- Text Mining for Insurance Claim Cost Prediction.- An Application of Time-Changing Feature Selection.- A Data Mining Approach to Analyze the Effect of Cognitive Style and Subjective Emotion on the Accuracy of Time-Series Forecasting.- A Multi-level Framework for the Analysis of Sequential Data.- 2: State-of-the-Art in Applications.- Hierarchical Hidden Markov Models: An Application to Health Insurance Data.- Identifying Risk Groups Associated with Colorectal Cancer.- Mining Quantitative Association Rules in Protein Sequences.- Mining X-Ray Images of SARS Patients.- The Scamseek Project - Text Mining for Financial Scams on the Internet.- A Data Mining Approach for Branch and ATM Site Evaluation.- The Effectiveness of Positive Data Sharing in Controlling the Growth of Indebtedness in Hong Kong Credit Card Industry.
[1]
J. Ross Quinlan,et al.
C4.5: Programs for Machine Learning
,
1992
.
[2]
Ron Kohavi,et al.
Wrappers for Feature Subset Selection
,
1997,
Artif. Intell..
[3]
Dimitrios Gunopulos,et al.
Automatic subspace clustering of high dimensional data for data mining applications
,
1998,
SIGMOD '98.
[4]
Dimitris Meretakis,et al.
Extending naïve Bayes classifiers using long itemsets
,
1999,
KDD '99.
[5]
Jack Sklansky,et al.
On Automatic Feature Selection
,
1988,
Int. J. Pattern Recognit. Artif. Intell..
[6]
Daniel A. Keim,et al.
An Efficient Approach to Clustering in Large Multimedia Databases with Noise
,
1998,
KDD.
[7]
Jiawei Han,et al.
Data Mining: Concepts and Techniques
,
2000
.
[8]
Peter E. Hart,et al.
Nearest neighbor pattern classification
,
1967,
IEEE Trans. Inf. Theory.
[9]
Ramakrishnan Srikant,et al.
Fast algorithms for mining association rules
,
1998,
VLDB 1998.
[10]
Ron Kohavi,et al.
Supervised and Unsupervised Discretization of Continuous Features
,
1995,
ICML.
[11]
Dimitar Filev,et al.
Generation of Fuzzy Rules by Mountain Clustering
,
1994,
J. Intell. Fuzzy Syst..
[12]
Usama M. Fayyad,et al.
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
,
1993,
IJCAI.
[13]
Jennifer Widom,et al.
Clustering association rules
,
1997,
Proceedings 13th International Conference on Data Engineering.
[14]
Stephen L. Chiu,et al.
Fuzzy Model Identification Based on Cluster Estimation
,
1994,
J. Intell. Fuzzy Syst..