WaveCluster: a wavelet-based clustering approach for spatial data in very large databases

Sheikholeslami, Gholamhosein; Chatterjee, Surojit; Zhang, Aidong

doi:10.1007/s007780050009

Cited by 400 publications

(330 citation statements)

References 22 publications

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“…So far, they are numerous studies on the clustering and a number of them are equipped with some mechanisms to detect outliers, such as CLARANS [16], DBSCAN [8], BIRCH [21], WaveCluster [20], DenClue [11] and CLIQUE [1]. Strictly speaking, clustering algorithms should not be considered as outlier detection methods, because their objective is only to group the objects in dataset such that clustering functions can be optimized.…”

Section: Related Workmentioning

confidence: 99%

Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance

2006

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Outlier detection is a fundamental step in knowledge discovery in databases. With the increasing number of high-dimensional databases, existing outlier detection algorithms that work only in the context of full space are unable to effectively screen out informative outliers. This is because majority of these outliers exists only in subspaces. In this paper, we identify a new outlier detection task for high-dimensional data, i.e. finding the subspaces in which given points are outliers, and propose a novel outlier detection algorithm, called High-D Outlier Detection (HighDOD). The intuitive idea is that we measure the outlying degree of the point using the sum of distances between this point and its k nearest neighbors. Two pruning strategies are proposed to realize fast pruning in the subspace search and an efficient dynamic subspace search method with a sample-based learning process has been implemented. Experimental results show that HighDOD is efficient and outperforms the naive top-down, bottom-up and random search methods.

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Section: Related Workmentioning

confidence: 99%

Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance

2006

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“…The advantage of this approach is the fast processing time that is in general independent of the number of data objects. The popular grid-based algorithms are STING [17], CLIQUE [18], and WaweCluster [19].…”

Section: Introductionmentioning

confidence: 99%

A Vibration Method for Discovering Density Varied Clusters

Elbatta

Bolbol

Ashour

2012

ISRN Artificial Intelligence

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DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the "core" of each cluster-clusters generated after applying DBSCAN. Then, it "vibrates" points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.

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“…From the grid based methods, WaveCluster [12] applies wavelet transforms to filter data. The authors assume that multidimensional spatial data objects can be represented in an n-dimensional feature space and that the dataset is an n-dimensional signal.…”

Section: Introductionmentioning

confidence: 99%

GeoSOM Suite: A Tool for Spatial Clustering

Henriques

Bação

Lobo

2009

Computational Science and Its Applications – ICCSA 2009

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Abstract. The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, which is based on Self-Organizing Maps. This paper describes this tool, and shows that it is adequate for exploring spatial data.

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WaveCluster: a wavelet-based clustering approach for spatial data in very large databases

Cited by 400 publications

References 22 publications

Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance

Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance

A Vibration Method for Discovering Density Varied Clusters

GeoSOM Suite: A Tool for Spatial Clustering

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