International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Models of incremental concept formation
Artificial Intelligence
Proceedings of the sixth international workshop on Machine learning
Description contrasting in incremental concept formation
EWSL-91 Proceedings of the European working session on learning on Machine learning
An incremental Bayesian algorithm for categorization
Concept formation knowledge and experience in unsupervised learning
The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
Learning to recognize movements
Concept formation knowledge and experience in unsupervised learning
Ordering effects in clustering
ML92 Proceedings of the ninth international workshop on Machine learning
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Acquiring and Combining Overlapping Concepts
Machine Learning - Special issue on computational models of human learning
A Branch and Bound Incremental Conceptual Clusterer
Machine Learning
Applying AI Clustering to Engineering Tasks
IEEE Expert: Intelligent Systems and Their Applications
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Knowledge acquisition via incremental conceptual clustering
Knowledge acquisition via incremental conceptual clustering
Cluster Analysis
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Communications of the ACM
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
A New Nonparametric Pairwise Clustering Algorithm Based on Iterative Estimation of Distance Profiles
Machine Learning - Special issue: Unsupervised learning
User Modeling for Personalized City Tours
Artificial Intelligence Review
An Experimental Comparison of Model-Based Clustering Methods
Machine Learning
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
SAINTETIQ: a fuzzy set-based approach to database summarization
Fuzzy Sets and Systems - Data bases and approximate reasoning
Dynamic Feature Selection in Incremental Hierarchical Clustering
ECML '00 Proceedings of the 11th European Conference on Machine Learning
KIDS: An Iterative Algorithm to Organize Relational Knowledge
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Abstractions for Knowledge Organization of Relational Descriptions
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Robust Incremental Clustering with Bad Instance Orderings: A New Strategy
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Temporal Probabilistic Concepts from Heterogeneous Data Sequences
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
A New Methodology to Compare Clustering Algorithms
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Learning from Multiple Bayesian Networks for the Revision and Refinement of Expert Systems
KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Efficient Local Search in Conceptual Clustering
DS '01 Proceedings of the 4th International Conference on Discovery Science
Feature Selection as Retrospective Pruning in Hierarchical Clustering
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Mining Clusters with Association Rules
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
An Experimental Study of Partition Quality Indices in Clustering
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Comparison of Three Objective Functions for Conceptual Clustering
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A Genetic Approach to Hierarchical Clustering of Euclidean Graphs
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Rearranging data objects for efficient and stable clustering
Proceedings of the 2005 ACM symposium on Applied computing
Maximum likelihood combination of multiple clusterings
Pattern Recognition Letters
Adaptive learning for event modeling and characterization
Pattern Recognition
Intelligent Data Analysis
A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING
Applied Artificial Intelligence
A new initialization method for categorical data clustering
Expert Systems with Applications: An International Journal
An initialization method for the K-Means algorithm using neighborhood model
Computers & Mathematics with Applications
Adaptive Web SitesA Knowledge Extraction from Web Data Approach
Proceedings of the 2008 conference on Adaptive Web Sites: A Knowledge Extraction from Web Data Approach
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
Propositionalization for clustering symbolic relational descriptions
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
INCRAIN: an incremental approach for the gravitational clustering
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Integrating induction and deduction for noisy data mining
Information Sciences: an International Journal
Finding semantic structures in image hierarchies using Laplacian graph energy
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Solving the Euclidean k-median problem by DCA
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
A machine learning and data mining framework to enable evolutionary improvement in trauma triage
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Hierarchical clustering based on mathematical optimization
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An incremental document clustering for the large document database
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Free play in contemplative ambient intelligence
AmI'11 Proceedings of the Second international conference on Ambient Intelligence
An incremental document clustering algorithm based on a hierarchical agglomerative approach
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Improving hierarchical document cluster labels through candidate term selection
Intelligent Decision Technologies
The effectiveness of lloyd-type methods for the k-means problem
Journal of the ACM (JACM)
Expert Systems with Applications: An International Journal
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Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a 'tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts - often according to task-specific criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to 'externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.