Algorithms for clustering data
Algorithms for clustering data
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Supporting Data Mining of Large Databases by Visual Feedback Queries
Proceedings of the Tenth International Conference on Data Engineering
Proceedings of the 17th International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Hypothetical knowledge and counterfactual reasoning
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
A visualization model of interactive knowledge discovery systems and its implementations
Information Visualization
Visualizing changes in the structure of data for exploratory feature selection
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Visually mining and monitoring massive time series
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
Interactive Decision Tree Construction for Interval and Taxonomical Data
Visual Data Mining
Towards Effective Visual Data Mining with Cooperative Approaches
Visual Data Mining
Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Domain-Driven Data Mining: Methodologies and Applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
High dimensional visual data classification
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
Domain-Driven actionable knowledge discovery in the real world
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Visual interactive evolutionary algorithm for high dimensional data clustering and outlier detection
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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The primary aim of most data mining algorithms is to facilitate the discovery of concise and interpretable information from large amounts of data. However, many of the current formalizations of data mining algorithms have not quite reached this goal. One of the reasons for this is that the focus on using purely automated techniques has imposed several constraints on data mining algorithms. For example, any data mining problem such as clustering or association rules requires the specification of particular problem formulations, objective functions, and parameters. Such systems fail to take the user's needs into account very effectively. This makes it necessary to keep the user in the loop in a way which is both efficient and interpretable. One unique way of achieving this is by leveraging human visual perceptions on intermediate data mining results. Such a system combines the computational power of a computer and the intuitive abilities of a human to provide solutions which cannot be achieved by either. This paper will discuss a number of recent approaches to several data mining algorithms along these lines.