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Knowledge acquisition via incremental conceptual clustering
- 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
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Cited by 765 (9 self)
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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
Concept formation by incremental conceptual clustering
- In Proceedings of the International Joint Conference Artificial Intelligence
, 1989
"... Incremental conceptual clustering is an important area of machine learning. It is concerned with summarizing data in a form of concept hierarchies, which will eventually ease the problem of knowledge acquisition for knowledge-based systems. In this paper we have described INC, a program that generat ..."
Abstract
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Cited by 12 (0 self)
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Incremental conceptual clustering is an important area of machine learning. It is concerned with summarizing data in a form of concept hierarchies, which will eventually ease the problem of knowledge acquisition for knowledge-based systems. In this paper we have described INC, a program
Incremental Conceptual Clustering Without Order Dependancy
, 1995
"... In this thesis, a new system for incremental conceptual clustering is presented. Incremental conceptual clustering systems integrate the learning with performance and obey the basic principles of human concept learning. As in other incremental learning systems, they are faced with the problem of dep ..."
Abstract
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Cited by 1 (0 self)
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In this thesis, a new system for incremental conceptual clustering is presented. Incremental conceptual clustering systems integrate the learning with performance and obey the basic principles of human concept learning. As in other incremental learning systems, they are faced with the problem
BIRCH: an efficient data clustering method for very large databases
- In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD
, 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem of ..."
Abstract
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Cited by 576 (2 self)
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of large datasets and minimization of 1/0 costs. This paper presents a data clustering method named Bfll (;”H (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and clynamicall y clusters incoming
Web Document Clustering: A Feasibility Demonstration
, 1998
"... Abstract Users of Web search engines are often forced to sift through the long ordered list of document “snippets” returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on the major s ..."
Abstract
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Cited by 435 (3 self)
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that clusters based on snippets are almost as good as clusters created using the full text of Web documents. To satisfy the stringent requirements of the Web domain, we introduce an incremental, linear time (in the document collection size) algorithm called Suffix Tree Clustering (STC). which creates clusters
A Growing Neural Gas Network Learns Topologies
- Advances in Neural Information Processing Systems 7
, 1995
"... An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this m ..."
Abstract
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Cited by 401 (5 self)
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An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994
Genetic Network Inference: From Co-Expression Clustering To Reverse Engineering
, 2000
"... motivation: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using highthroughput gene expression assays, we are able to measure the output of the ge ..."
Abstract
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Cited by 336 (0 self)
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of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various
Cities and the creative class.
- City and Community,
, 2003
"... Cities and regions have long captured the imagination of sociologists, economists, and urbanists. From Alfred Marshall to Robert Park and Jane Jacobs, cities have been seen as cauldrons of diversity and difference and as fonts for creativity and innovation. Yet until recently, social scientists con ..."
Abstract
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Cited by 359 (1 self)
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concerned with regional growth and development have focused mainly on the role of firms in cities, and particularly on how these firms make location decisions and to what extent they concentrate together in agglomerations or clusters. This short article summarizes recent advances in our thinking about
DATA MINING USING CONCEPTUAL CLUSTERING
"... 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
Incremental Clustering and Dynamic Information Retrieval
, 1997
"... Motivated by applications such as document and image classification in information retrieval, we consider the problem of clustering dynamic point sets in a metric space. We propose a model called incremental clustering which is based on a careful analysis of the requirements of the information retri ..."
Abstract
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Cited by 191 (4 self)
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Motivated by applications such as document and image classification in information retrieval, we consider the problem of clustering dynamic point sets in a metric space. We propose a model called incremental clustering which is based on a careful analysis of the requirements of the information
Results 1 - 10
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