G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of VLDB, pages 428--439, August 1998. Home/Search Document Details and Download
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On Data Clustering Analysis: Scalability, Constraints.. - Zaïane, Foss.. (Correct)
....This section gives a taxonomy analysis and an experimental study of representative methods in each group. In order to examine the clustering ability of clustering algorithms, we performed experimental evaluation upon k means [12] CURE [21] ROCK [8] DBSCAN [2] CHAMELEON [14] WaveCluster [24] and CLIQUE [1] The DBSCAN code came from its authors while CURE and ROCK codes were kindly supplied by the Department of Computer Science and Engineering, University of Minnesota. k means, CHAMELEON, WaveCluster, and CLIQUE programs were locally implemented. We evaluate these algorithms by using ....
....Engineering, University of Minnesota. k means, CHAMELEON, WaveCluster, and CLIQUE programs were locally implemented. We evaluate these algorithms by using two dimensional spatial data sets referenced and used in the CHAMELEON paper [14] and data sets referenced and used in the WaveCluster paper [24]. The reason for using two dimensional spatial data is because we can visually evaluate the quality of the clustering result. Often people can intuitively identify clusters on two dimensional spatial data, while this is usually very di#cult for high dimensional data sets. We show the experimental ....
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Sheikholeslami G., Chatterjee S. and Zhang A. (1998) Wavecluster: a multiresolution clustering approach for very large spatial databases. In Proc. 24th Conf. on Very Large Data Bases.
On Complementarity of Cluster and Outlier - Detection Schemes Zhixiang (Correct)
....mechanism. Most methods in the early work that detects outliers independently have been developed in the field of statistics [3] These methods normally assume that the distribution of a data set is known in advance. A large amount of the work was done under the general topic of clustering [6, 12, 15, 8, 17]. These algorithms can also generate outliers as by products. Recently, researchers have proposed distance based and density based as well as connectivity based outlier detection schemes [10, 11, 13, 4, 16] which distinguish objects that are likely to be outliers from those that are not based on ....
G. Sheikholeslami, S. Chatterjee, A. Zhang: "WaveCluster: A multi-Resolution Clustering Approach for Very Large Spatial Databases", Proc. of 24th Intl. Conf. On Very Large Data Bases, 1998, pp 428 - 439.
A Parameterless Method for Efficiently Discovering Clusters.. - In Large Datasets (Correct)
....processing speed only depends on the resolution of griding and not on the size of the data set. The grid based methods are more suitable for high density data sets with huge number of data objects in limited space. Representative grid based algorithms include CLIQUE [1] and, recently, WaveCluster [12] which applies a noise filter and wavelet transformation. Grid based quantization of the data space speeds up the processing, but due to the rectangular structure, the algorithm represents a much coarser approach to levels of resolution than that adopted by our algorithm TURN presented herein, or ....
....speed and scalability since the data is read in and quantized and then subsequent processing is on a much smaller effective data size. While WaveCluster claims to be parameter free, our research showed that its clustering results are quite sensitive to the settings that have to be made. In fact [12] points out that knowing the number of clusters to be found is very helpful for choosing the parameters for WaveCluster. 2.1. Unsupervised Method: TURN Previously, we have devised a non parametric approach to categorical data clustering in a non Euclidean space called TURN that we used for ....
[Article contains additional citation context not shown here]
G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: a multi-resolution clustering approach for very large spatial databases. In 24th VLDB Conference, New York, USA, 1998.
Fast Spatial Clustering with Different Metrics and in the.. - Estivill-Castro, Lee (2001) (Correct)
....metric) These distinct groups of high spatial concentrations are indicative of phenomena with spatial association and their explanation results into useful insights or they are suggestive of hypotheses providing the ground for further exploratory analysis. Numerous spatial clustering approaches [3, 4, 5, 10, 17, 19, 20, 21] have been studied within the GIS community. They have di erent strengths and weaknesses. Typically, spatial clustering methods share several common drawbacks. First, they are not fully automatic. Namely, these semi automatic clustering methods necessitate some prior knowledge from end users to ....
G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24rd International Conference on Very Large Data Bases (VLDB), pages 428-439, 1998.
SyMP: An Efficient Clustering Approach to Identify Clusters Of.. - Frigui (2002) (Correct)
....since the intrinsic structure of all clusters cannot be characterized by global density parameters. Other density based algorithms include DBCLASD[20] a density based algorithm that assumes that points within a cluster are uniformly distributed, STING[19] an enhancement of DBSCAN, WaveCluster[18], a method based on wavelets, and DENCLUE[10] which uses influence functions to model the points density. CLIQUE[1] is a region grouping algorithm that can find clusters embedded in subspaces of the original high dimensional data space. BIRCH[21] and CURE[8] are two hierarchical algorithms that ....
G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proc. VLDB, 1998.
Metadata Generation and Retrieval of Geographic Imagery - Aidong Zhang And (2001) Self-citation (Zhang) (Correct)
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th VLDB conference, pages 428--439, August 1998.
A Shrinking-Based Approach for Multi-Dimensional Data - Analysis Yong Shi (2003) Self-citation (Zhang) (Correct)
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of the 24th International Conference on Very Large Data Bases, 1998.
Efficiently Detecting Arbitrary Shaped Clusters in Image.. - Yu, Chatterjee, Zhang (1998) (1 citation) Self-citation (Chatterjee Zhang) (Correct)
.... into a finite number of cells and then do all operations on the quantized space [13, 7] CLIQUE clustering algorithm identifies dense clusters in subspaces of maximum dimensionality [1] We proposed WaveCluster which partitions the feature space into cells and applies wavelet transform on them [11]. However, the existing clustering algorithms either require the number of clusters to be known a priori or are not designed to deal with the image feature vectors in highdimensional space (dimensions = 64, 128, or 256) Due to the Curse of Dimensionality [2] Exponential dependency of measures ....
....amplitude correspond to the areas of the feature space where the objects are concentrated. These are the clusters in the original space. This view of the feature space allows us to use signal processing techniques such as wavelet transform to decompose the signal into different frequency bands. In [11] it has been shown that wavelet transform techniques are very effective and efficient for low dimensional data. WaveCluster algorithm can detect arbitrary shape clusters. It also has the multiresolution property. But this method runs into problem when number of dimensions is very high. For ....
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th VLDB conference, pages 428-- 439, New York City, August 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of VLDB, pages 428--439, August 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang, "WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases," Proc.Int.Conf.VeryLarge Databases, 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the Twenty-Fourth Very Large Database Conference, pages 428--439, 1998.
Enhancing Data Analysis with Noise Removal - Hui Xiong Member (Correct)
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of International Conference on Very Large Databases, 1998.
Enhancing Data Analysis with Noise Removal - Hui Xiong Student (Correct)
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of International Conference on Very Large Databases, 1998.
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SHEIKHOLESLAMI G. etc. WaveCluster: A multiresolution clustering approach for very large spatial databases. In Proceedings of the 24th Conference on VLDB, 428-439.
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Sheikholeslami, G., Chatterjee, S. & Zhang, A. (1998), WaveCluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of the 24th International Conference of Very Large Data Bases (VLDB), pp. 428--439.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: a multiresolution clustering approach for very large spatial databases. In 24th VLDB Conference, New York, USA, 1998. 16
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G. Sheikholeslami, S. Chatterjee, and A. Zhang, "WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases," Proc. Very Large Date Bases Conf., pp. 428-439, Aug. 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang, "WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases," Proc. Very Large Date Bases Conf., pp. 428-439, Aug. 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang, "WaveCluster: a multiresolution clustering approach for very large spatial databases," in Proc. VLDB Conf., New York, August 1998, pp. 428--439.
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G. Sheikholeslami, S. Chatterjee, A. Zhang, "WaveCluster: A multi-resolution clustering approach for very large spatial databases" Proc. of the VLDB Conf., pp. 428-439, New York City, August 1998.
Studying Digital Imagery of Ancient Paintings by Mixtures of.. - Li, Wang (2003) (Correct)
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G. Sheikholeslami, S. Chatterjee, and A. Zhang, "WaveCluster: A multiresolution clustering approach for very large spatial databases" Proc. of the VLDB Conf., pp. 428-439, New York City, August 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proc. VLDB, pages 428--439, 1998.
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Sheikholeslami G., Chatterjee S., Zhang A.: "WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases", Proc. VLDB'98, New York, NY, 1998, pp. 428 - 439.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Pro. of the 24rd Int. Conf. on Very Large Data Bases, pages 428-439, 1998.
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G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases, 1998.
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