The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
C4.5: programs for machine learning
C4.5: programs for machine learning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Pixel-oriented database visualizations
ACM SIGMOD Record
Narcissus: visualising information
Readings in information visualization
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
DIVE-ON: from databases to virtual reality
Crossroads
Visualization Support for Data Mining
IEEE Expert: Intelligent Systems and Their Applications
Visualising sequences of queries: a new tool for information retrieval
IV '97 Proceedings of the IEEE Conference on Information Visualisation
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
TeraScope: distributed visual data mining of terascale data sets over photonic networks
Future Generation Computer Systems - iGrid 2002
Detecting Patterns of Change Using Enhanced Parallel Coordinates Visualization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Visualization of Rule's Similarity using Multidimensional Scaling
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A visualization model of interactive knowledge discovery systems and its implementations
Information Visualization
V-Miner: using enhanced parallel coordinates to mine product design and test data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
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In data mining, or knowledge discovery, we are essentially faced with a mass of data that we are trying to make sense of. We are looking for something “interesting”. Quite what “interesting” means is hard to define, however – one day it is the general trend that most of the data follows that we are intrigued by – the next it is why there are a few outliers to that trend. In order for a data mining to be generically useful to us, it must therefore have some way in which we can indicate what is interesting and what is not, and for that to be dynamic and changeable.