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Knowledge acquisition via incremental conceptual clustering

by Douglas H. Fisher - Machine Learning , 1987
"... 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 ..."
Abstract - Cited by 765 (9 self) - Add to MetaCart
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

and Conceptual Clustering

by unknown authors
"... This paper presents how Multistrategy Error Detection and Discovery (MEDD), a student modeling system using machine learning can be applied to the domain of Object Oriented Programming. Java is the language used in learning object oriented programming. MEDD detects the learner’s errors and discovers ..."
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This paper presents how Multistrategy Error Detection and Discovery (MEDD), a student modeling system using machine learning can be applied to the domain of Object Oriented Programming. Java is the language used in learning object oriented programming. MEDD detects the learner’s errors and discovers the misconceptions based on the presence (or absence) of errors.

Conceptual Clustering of Text Clusters

by Andreas Hotho, Gerd Stumme - In Proceedings of FGML Workshop , 2002
"... Common clustering techniques have the disadvantage that they do not provide intensional descriptions of the clusters obtained. Conceptual Clustering techniques, on the other hand, provide such descriptions, but are known to be rather slow. In this paper, we discuss a way of combining both techniqu ..."
Abstract - Cited by 19 (3 self) - Add to MetaCart
Common clustering techniques have the disadvantage that they do not provide intensional descriptions of the clusters obtained. Conceptual Clustering techniques, on the other hand, provide such descriptions, but are known to be rather slow. In this paper, we discuss a way of combining both

CONCEPTS IN CONCEPTUAL CLUSTERING

by unknown authors
"... Although it has a relatively short history, conceptual clustering is an especially active area of research in machine learning. There are a variety of ways in which conceptual patterns (the Al contribution to clustering) play a role in the clustering process. Two distinct conceptual clustering parad ..."
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Although it has a relatively short history, conceptual clustering is an especially active area of research in machine learning. There are a variety of ways in which conceptual patterns (the Al contribution to clustering) play a role in the clustering process. Two distinct conceptual clustering

Noise-Tolerant Conceptual Clustering*

by unknown authors
"... Fisher (1987a,b) introduced a performance task for conceptual clustering: flexible prediction of arbitrary attribute values, not simply the prediction of a single 'class' attribute. This paper extends earlier analysis by considering the effects of noise and other environmental factors. The ..."
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Fisher (1987a,b) introduced a performance task for conceptual clustering: flexible prediction of arbitrary attribute values, not simply the prediction of a single 'class' attribute. This paper extends earlier analysis by considering the effects of noise and other environmental factors

Efficient Feature Selection in Conceptual Clustering

by Mark Devaney, Ashwin Ram - In Proceedings of the Fourteenth International Conference on Machine Learning , 1997
"... Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised ..."
Abstract - Cited by 52 (0 self) - Add to MetaCart
Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised

Conceptual Clustering in Information Retrieval

by Sanjiv K. Bhatia, Jitender S. Deogun - IEEE Transactions on Systems, Man and Cybernetics , 1998
"... Clustering is used in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are associated with each other are assigned to the same cluster. This a ..."
Abstract - Cited by 22 (1 self) - Add to MetaCart
Clustering is used in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are associated with each other are assigned to the same cluster

Graph-based hierarchical conceptual clustering

by Istvan Jonyer, Lawrence B. Holder, Diane J. Cook - International Journal on Artificial Intelligence Tools , 2001
"... Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we ..."
Abstract - Cited by 32 (5 self) - Add to MetaCart
Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we

DATA MINING USING CONCEPTUAL CLUSTERING

by Khaled Hammouda
"... The task of data mining is mainly concerned with the extraction of knowledge from large sets of data. Clustering techniques are usually used to find regular structures in data. Conceptual clustering is one technique that forms concepts out of data incrementally by subdividing groups into subclasses ..."
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The task of data mining is mainly concerned with the extraction of knowledge from large sets of data. Clustering techniques are usually used to find regular structures in data. Conceptual clustering is one technique that forms concepts out of data incrementally by subdividing groups into subclasses

A New Conceptual Clustering Framework

by Nina Mishra, Dana Ron, Ram Swaminathan - MACHINE LEARNING , 2004
"... We propose a new formulation of the conceptual clustering problem where the goal is to explicitly output a collection of simple and meaningful conjunctions of attributes that define the clusters. The formulation differs from previous approaches since the clusters discovered may overlap and also may ..."
Abstract - Cited by 14 (2 self) - Add to MetaCart
We propose a new formulation of the conceptual clustering problem where the goal is to explicitly output a collection of simple and meaningful conjunctions of attributes that define the clusters. The formulation differs from previous approaches since the clusters discovered may overlap and also may
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