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158
Data Clustering: A Review
 ACM COMPUTING SURVEYS
, 1999
"... Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exp ..."
Abstract

Cited by 1282 (13 self)
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Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify crosscutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
Automatic Subspace Clustering of High Dimensional Data
 Data Mining and Knowledge Discovery
, 2005
"... Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, enduser comprehensibility of the results, nonpresumption of any canonical data distribution, and insensitivity to the or ..."
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Cited by 560 (12 self)
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Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, enduser comprehensibility of the results, nonpresumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate clusters in large high dimensional datasets.
Efficient Clustering of HighDimensional Data Sets with Application to Reference Matching
, 2000
"... Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a sm ..."
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Cited by 256 (12 self)
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Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at oncefor example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. Under r...
Approximation Algorithms for Projective Clustering
 Proceedings of the ACM SIGMOD International Conference on Management of data, Philadelphia
, 2000
"... We consider the following two instances of the projective clustering problem: Given a set S of n points in R d and an integer k ? 0; cover S by k hyperstrips (resp. hypercylinders) so that the maximum width of a hyperstrip (resp., the maximum diameter of a hypercylinder) is minimized. Let w ..."
Abstract

Cited by 245 (21 self)
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We consider the following two instances of the projective clustering problem: Given a set S of n points in R d and an integer k ? 0; cover S by k hyperstrips (resp. hypercylinders) so that the maximum width of a hyperstrip (resp., the maximum diameter of a hypercylinder) is minimized. Let w be the smallest value so that S can be covered by k hyperstrips (resp. hypercylinders), each of width (resp. diameter) at most w : In the plane, the two problems are equivalent. It is NPHard to compute k planar strips of width even at most Cw ; for any constant C ? 0 [50]. This paper contains four main results related to projective clustering: (i) For d = 2, we present a randomized algorithm that computes O(k log k) strips of width at most 6w that cover S. Its expected running time is O(nk 2 log 4 n) if k 2 log k n; it also works for larger values of k, but then the expected running time is O(n 2=3 k 8=3 log 4 n). We also propose another algorithm that computes a c...
A Framework for Clustering Evolving Data Streams
 In VLDB
, 2003
"... The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a... ..."
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Cited by 239 (32 self)
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The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a...
Storing semistructured data with STORED
"... Systems for managing and querying semistructureddata sources often store data in proprietary object repositories or in a taggedtext format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between ..."
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Cited by 229 (8 self)
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Systems for managing and querying semistructureddata sources often store data in proprietary object repositories or in a taggedtext format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between the semistructured data model and the relational data model, expressed in a query language called STORED. When a semistrcutured data instance is given, a STORED mapping can be generated automatically using datamining techniques. We are interested in applying STORED to XML data, which is an instance of semistructured data. We show how a documenttypedescriptor (DTD), when present, can be exploited to further improve performance.
Efficient Algorithms for Mining Outliers from Large Data Sets
"... In this paper, we propose a novel formulation for distancebased 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 ..."
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Cited by 217 (1 self)
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In this paper, we propose a novel formulation for distancebased 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 to developing relatively straightforward solutions to finding such outliers based on the classical nestedloop join and index join algorithms, we develop a highly efficient partitionbased algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on reallife and synthetic data sets. The results from a reallife NBA database highlight and reveal several expected and unexpected aspects of the database. The results from a study on synthetic data sets demonstrate that the partitionbased algorithm scales well with respect to both data set size and data set dimensionality. 1
Wavecluster: A multiresolution clustering approach for very large spatial databases
, 1998
"... Many applications require the management of spatial data. Clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach s ..."
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Cited by 170 (5 self)
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Many applications require the management of spatial data. Clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shape. It must be insensitive to the outliers (noise) and the order of input data. We propose WaveCluster, a novel clustering approach based on wavelet transforms, which satisfies all the above requirements. Using multiresolution property of wavelet transforms, we can effectively identify arbitrary shape clusters at different degrees of accuracy. We also demonstrate that WaveCluster is highly efficient in terms of time complexity. Experimental results on very large data sets are presented which show the efficiency and effectiveness of the proposed approach compared to the other recent clustering methods.
Outlier detection for high dimensional data
, 2001
"... The outlier detection problem has important applications in the eld of fraud detection, netw ork robustness analysis, and intrusion detection. Most suc h applications are high dimensional domains in whic hthe data can con tain hundreds of dimensions. Many recen t algorithms use concepts of pro ximit ..."
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Cited by 154 (4 self)
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The outlier detection problem has important applications in the eld of fraud detection, netw ork robustness analysis, and intrusion detection. Most suc h applications are high dimensional domains in whic hthe data can con tain hundreds of dimensions. Many recen t algorithms use concepts of pro ximity in order to nd outliers based on their relationship to the rest of the data. Ho w ever, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective ofproximitybased de nitions. Consequently, for high dimensional data, the notion of nding meaningful outliers becomes substantially more complex and nonobvious. In this paper, w e discuss new techniques for outlier detection whic h nd the outliers by studying the behavior of projections from the data set. 1.
Finding Generalized Projected Clusters in High Dimensional Spaces
"... High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimensional data, even the concept of proximity or clustering may not be meaningful. We discuss very general techniques for projec ..."
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Cited by 139 (8 self)
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High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimensional data, even the concept of proximity or clustering may not be meaningful. We discuss very general techniques for projected clustering which are able to construct clusters in arbitrarily aligned subspaces of lower dimensionality. The subspaces are specific to the clusters themselves. This definition is substantially more general and realistic than currently available techniques which limit the method to only projections from the original set of attributes. The generalized projected clustering technique may also be viewed as a way of trying to rede ne clustering for high dimensional applications by searching for hidden subspaces with clusters which are created by interattribute correlations. We provide a new concept of using extended cluster feature vectors in order to make the algorithm scalable for very large databases. The running time and space requirements of the algorithm are adjustable, and are likely to tradeoff with better accuracy.