hill climbing Abstract. Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains. 1.
|
901
|
The Sciences of the Artificial
– Simon
|
|
483
|
Generalization as search
– Mitchell
- 1982
|
|
298
|
Cluster Analysis
– Everitt
- 1980
|
|
278
|
Learning efficient classification procedures and their application to chess end games
– Quinlan
- 1983
|
|
221
|
Categories and concepts
– Smith, Medin
- 1981
|
|
207
|
Learning from observation: Conceptual clustering
– Michalski
- 1983
|
|
103
|
A comparative review of selected methods for learning from examples
– Dietterich
- 1983
|
|
99
|
Elements of Machine Learning
– Langley
- 1996
|
|
75
|
A case study of incremental concept induction
– Schlimmer, Fisher
- 1986
|
|
72
|
Information, uncertainty, and the utility of categories
– Gluck, Corter
- 1985
|
|
67
|
In defense of probability
– Cheeseman
- 1985
|
|
52
|
Categorization of natural objects
– Mervis, Rosch
- 1981
|
|
45
|
Approaches to conceptual clustering
– Fisher, Langley
- 1985
|
|
41
|
Reconstructive memory a computer model
– Kolodner
- 1983
|
|
39
|
A general framework for induction and a study of selective induction
– Rendell
- 1986
|
|
39
|
Beyond incremental processing: Tracking concept drift
– Schlimmer, Jr
- 1986
|
|
38
|
Knowledge acquisition through conceptual clustering: A theoretical framework and an algorithm for partitioning data into conjunctive concepts
– Michalski
- 1980
|
|
32
|
Learning and inductive inference
– Dietterich
- 1982
|
|
26
|
EPAM-like models of recognition and learning
– Feigenbaum, Simon
- 1984
|
|
24
|
Concept learning in a rich input domain: Generalization-based memory
– Lebowitz
- 1987
|
|
24
|
Integrated learning: controlling explanation
– Lebowitz
- 1986
|
|
22
|
Classification Problem Solving
– Clancey
- 1984
|
|
22
|
Constraints and preferences in inductive learning: An experimental study of human and machine performance
– Medin, Wattenmaker
- 1987
|
|
21
|
Conjunctive conceptual clustering: A methodology and experimentation
– Stepp
- 1984
|
|
20
|
I lied about the trees
– Brachman
- 1985
|
|
15
|
Learning intermediate concepts in constructing a hierarchical knowledge base
– Fu, Buchanan
- 1985
|
|
13
|
Inductive Learning of Relational Productions
– Vere
- 1978
|
|
9
|
Conceptual clustering in knowledge organization
– Cheng, Fu
- 1985
|
|
9
|
Correcting erroneous generalizations
– Lebowitz
- 1982
|
|
7
|
Conceptual clustering as discrimination learning
– Langley, Sage
- 1984
|
|
6
|
Methods of Conceptual Clustering and their Relation to Numerical Taxonomy
– Fisher, Langley
- 1986
|
|
4
|
A Hierarchical Conceptual Clustering Algorithm
– Fisher
- 1984
|
|
4
|
Learning hidden causes from empirical data
– Pearl
- 1985
|
|
3
|
The world modeler's project: Objectives and Simulator Architecture
– Carbonell, Hood
- 1986
|
|
3
|
Machine learning, clustering and polymorphy
– Hanson, Bauer
- 1986
|
|
3
|
Incremental Learning of Concept Descriptions: A
– Reinke, Michalski
- 1988
|
|
3
|
Conceptual clustering: Inventing goaldirected classifications of structured objects, volume 2
– Stepp, Michalski
- 1986
|
|
2
|
Components of Learning in a reactive environment
– Langley, Kibler, et al.
- 1986
|
|
1
|
Models of incremental concept formation (Technical Report
– Gennari, Langley, et al.
- 1987
|
|
1
|
Learning concepts in a complex robot world
– Sammut, Hume
- 1986
|
