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Results 1 - 10
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148,984
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
A New Cluster Algorithm for Graphs
- National Research Institute for Mathematics and Computer Science in the
, 1998
"... A new cluster algorithm for graphs called the Markov Cluster algorithm (MCL algorithm) is introduced. ..."
Abstract
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Cited by 77 (2 self)
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A new cluster algorithm for graphs called the Markov Cluster algorithm (MCL algorithm) is introduced.
CURE: An Efficient Clustering Algorithm for Large Data sets
- Published in the Proceedings of the ACM SIGMOD Conference
, 1998
"... Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering ..."
Abstract
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Cited by 722 (5 self)
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Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new
Survey of clustering algorithms
- IEEE TRANSACTIONS ON NEURAL NETWORKS
, 2005
"... Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the ..."
Abstract
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Cited by 499 (4 self)
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, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts
A density-based algorithm for discovering clusters in large spatial databases with noise
, 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
Abstract
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Cited by 1786 (70 self)
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of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover
A New Clustering Algorithm On Nominal Data Sets
"... Abstract—This paper presents a new clustering technique named as the Olary algorithm, which is suitable to cluster nominal data sets. This algorithm uses a new code with the name of the Olary code to transform nominal attributes into integer ones through a process named as the Olary transformation. ..."
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Abstract—This paper presents a new clustering technique named as the Olary algorithm, which is suitable to cluster nominal data sets. This algorithm uses a new code with the name of the Olary code to transform nominal attributes into integer ones through a process named as the Olary transformation
Ant colony metaphor in a new clustering algorithm ∗ by
"... Abstract: Among the many bio–inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. Clustering with ..."
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with swarm–based algorithms is emerging as an alternative to more conventional clustering methods, such as k–means algorithm. This proposed approach mimics the clustering behavior observed in real ant colonies. As a case study, this paper focuses on the behavior of clustering procedures in this new approach
ABSTRACT A New Cluster Algorithm for Graphs
, 1998
"... and their applications. SMC is sponsored by the Netherlands Organization for Scientific Research (NWO). CWI is a member of ..."
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and their applications. SMC is sponsored by the Netherlands Organization for Scientific Research (NWO). CWI is a member of
A New Clustering Algorithm Based on the Chemical Recognition System of Ants
, 2002
"... In this paper, we introduce a new method to solve the unsupervised clustering problem, based on a modelling of the chemical recognition system of ants. This system allow ants to discriminate between nestmates and intruders, and thus to create homogeneous groups of individuals sharing a similar odor ..."
Abstract
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Cited by 27 (3 self)
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by continuously exchanging chemical cues. This phenomenon, known as "colonial closure", inspired us into developing a new clustering algorithm and then comparing it to a well-known method such as K-MEANS method. Our results show that our algorithm performs better than K-MEANS over artificial and real
On Spectral Clustering: Analysis and an algorithm
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract
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Cited by 1713 (13 self)
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Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors
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