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348,957
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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of random sampling and partitioning. A random sample drawn from the data set is first partitioned and each partition is partially clustered. The partial clusters are then clustered in a second pass to yield the desired clusters. Our experimental results confirm that the quality of clusters produced by CURE
Multiview Registration for Large Data Sets
, 1999
"... In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitabl ..."
Abstract
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Cited by 222 (1 self)
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suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used
Visualising Large Data Sets
, 2006
"... Large data sets are different and new methods of display are needed for dealing with them. This paper reviews the standard problems in displaying large numbers of cases and variables, both continuous and categorical, and emphasises the need for improving current software. Much could be achieved by a ..."
Abstract
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Cited by 9 (0 self)
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Large data sets are different and new methods of display are needed for dealing with them. This paper reviews the standard problems in displaying large numbers of cases and variables, both continuous and categorical, and emphasises the need for improving current software. Much could be achieved
for Large Data-set
, 2012
"... Abstract:-Clustering Performance is based iterative and analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of the methodology is search to be near and its close to the desired cluster centers in each step attributes. This paper has been proposes a ..."
Abstract
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of computation can be reduced with a group in runtime constructed data sets are very promising.Modified Approach of K Mean Algorithm is Better then K Mean for Large Data Sets..
Reasoning with Large Data Sets
"... Abstract. Efficient reasoning is a critical factor for successful Semantic Web applications. In this context, applications may require vast volumes of data to be processed in a short time. We develop novel reasoning techniques which will extend current reasoning methods as well as existing database ..."
Abstract
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Abstract. Efficient reasoning is a critical factor for successful Semantic Web applications. In this context, applications may require vast volumes of data to be processed in a short time. We develop novel reasoning techniques which will extend current reasoning methods as well as existing database
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
, 1998
"... The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm to categoric ..."
Abstract
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Cited by 264 (3 self)
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The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. In this paper we present two algorithms which extend the k-means algorithm
Efficient Algorithms for Mining Outliers from Large Data Sets
, 2000
"... In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its k th nearest neighbor. We rank each point on the basis of its distance to its k th nearest neighbor and declare the top n points in this ranking to be outliers. In addition ..."
Abstract
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Cited by 322 (0 self)
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. In addition to developing relatively straightforward solutions to finding such outliers based on the classical nestedloop join and index join algorithms, we develop a highly efficient partition-based algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets
Automated Extraction and Parameterization of Motions in Large Data Sets
- ACM Transactions on Graphics
, 2004
"... Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively ..."
Abstract
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Cited by 183 (2 self)
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Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively
Results 1 - 10
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348,957
