J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Minining and Knowledge Discovery, 2(2):169--194, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Euclidean, or other numerical oriented, distance measures cannot be applied. In the literature, we find approaches close to both partitional and hierarchical methods. For each category, there exists a plethora of sub categories, e.g. density based clustering oriented towards geographical data [SEKX98], and algorithms for finding clusters. An exception to this is the class of categorical data approaches. Visualization of such data is not straightforward and there is no inherent geometrical structure in them, hence the approaches that have appeared in the literature mainly use concepts carried ....

J org Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. Density-Based Clustering in Spatial Databases: The Algortihm GDBSCAN and Its Applications. Workshop on Research Issues on Data Mining and Knowledge Discovery, (DMKD), 2(2): 169--194, 1998.

....However, the fact that data mining algorithms analyze the whole data set and, therefore, raise a high number of similarity queries can be particularly taken into account. In [BBBK 00] we analyzed a high number of data mining algorithms, particularly the density based clustering algorithm DBSCAN [SEKX 98] and the hierarchical cluster analysis method OPTICS [ABKS 99] which raise a similarity query for each object stored in the database. We showed that such algorithms can also be based on top of a database primitive called similarity join. The similarity join PQof two finite sets P = p 1 , p n ....

Sander J., Ester M., Kriegel H.-P., Xu X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Vol. 2, No. 2, 1998.

....related to specific fields. Clustering in data mining was brought to life by intense developments in information retrieval and text mining (Cutting et al. CKPT92] Steinbach et al. SKK00] Dhillon et al. DFG01] spatial database applications, for example GIS, Xu et al. XEKS98] Sander et al. [SEKX98], Ester et al. EFKS00] sequence and heterogeneous data analysis (Cadez et al. CSM01] Web applications (Cooley et al. CMS99] Heer Chi [HC01] Foss et al. FWZ01] DNA analysis in computational biology (Ben Dor Yakhini [BY99] and many others. They resulted in a large amount of ....

....as polygons. Any reflexive and symmetric predicate (e.g. two polygons have a non empty intersection) suffice to define a neighborhood . Additional measures (as intensity of a point) can be used instead of a simple count as well. These two generalizations lead to algorithm GDBSCAN (Sander et al. [SEKX98]) which uses the same two parameters as algorithm DBSCAN. With regard to these two parameters , MinPts, there is no straightforward way to fit them to data. Moreover, different parts of data could require different parameters the problem discussed earlier in conjunction with CnvmL[o. Ankerst et ....

Sander, J., Ester, M., Kriegel, H.-P., and Xu, X.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. In Data Mining and Knowledge Discovery, 2 (2), 169-194, 1998.

....distributions, each having different relevant attributes for generation of the target attribute. Each data set had 6561 patterns with 5 relevant attributes, where the degree of relevance was different for each distribution. Spatial clustering is performed using a density based algorithm DBSCAN [5], which was previously used in our centralized spatial regression modeling. As local regression models, we trained 2 layered feedforward neural network models with 5, 10 and 15 hidden neurons. We used Levenberg Marquardt [3] learning algorithm and repeated experiments starting from 3 random ....

Sander J., Ester M., Kriegel H.-P., Xu X.: "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications," Data Mining and Knowledge Discovery, An International Journal, Kluwer Academic Publishers, Vol. 2, No. 2, pp. 169-194, 1998.

....between x i and m j , although other distance measures can be used. The points k j j m 1 = are known as cluster centroids or cluster means. The second clustering algorithm called DBSCAN relies on a density based notion of clusters and was designed to discover clusters of an arbitrary shape [33]. The key idea of a density based cluster is that for each point of a cluster its Eps neighborhood for a given Eps 0 has to contain at least a minimum number of points (MinPts) i.e. the density in the Eps neighborhood of points has to exceed some threshold) since the typical density of points ....

Sander J., Ester M., Kriegel H-P, Xu X., Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, 2(2): 169-194, 1998.

....feature selection at step 0 of each boosting iteration, two clustering algorithms were employed to partition the spatial data set into similar regions. The first one called DBSCAN relies on a density based notion of clusters and was designed to discover clusters of an arbitrary shape efficiently [21]. The key idea of density based clustering is that for each point of a cluster its Eps neighborhood for a given Eps 0 has to contain at least a minimum number of points (MinPts) i.e. the density in the Eps neighborhood of points has to exceed some threshold) Furthermore, the typical density ....

J. SANDER, M. ESTER, H-P. KRIEGEL, X. XU, Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, Kluwer Academic Publishers, 2(1998), pp. 169-194.

....of data examples (total number of lesioned voxels) compared to parametric ones. Work in progress includes the examination of parametric methods for learning distributions, such as expectationmaximization algorithm [10] and clustering algorithms for partitioning distributions into distinct regions [28,29]. The proposed technique for the histogram computation involved the initial estimation on a coarse grid, followed by interpolation and smoothing in order to obtain histograms on the same grid as the underlying data. Since the voxel density is uniform in all three dimensions, during the estimation ....

Sander J., Ester M., Kriegel H-P., Xu X., Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Proc. On Data Mining and Knowledge Discovery, 2, 2, 169-194, (1998).

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Sander, J., Ester, M., Kriegel, H.P., Xu, X.: "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications". In: Data Mining and Knowledge Discovery, An International Journal, Kluwer Academic Publishers (1998) 169--194

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Sander J., Ester M., Kriegel H.-P., Xu X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998.

....layer with r neurons. Similarly, the last two layers of the NN define an inverse functional mapping from the r dimensional sub space back into the original d dimensional space. Using the features derived through the feature selection and extraction procedures, the DBSCAN clustering algorithm [2,9] was used to partition fields into similar regions ignoring the spatial attributes (x and y coordinates) and the yield value. The DBSCAN algorithm was applied to merged training and testing field data. These fields need not be adjacent as the x and y coordinates were ignored in the clustering ....

Sander J., Ester M., Kriegel H.-P., Xu X.: "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications," Data Mining and Knowledge Discovery, An International Journal, Kluwer Academic Publishers, Vol. 2, No. 2, pp. 169-194, 1998.

.... contains all point pairs where the distance does not exceed an user given parameter , i.e. As a consequence, many data mining algorithms can be directly performed on top of a similarity join as proposed in [BBBK 00] A typical example of such an algorithm is the clustering algorithm DBSCAN [SEKX 98] This algorithm defines a point p of the database to be a core point with respect to the user given parameters and min pts if at least a number min pts of the points in the database have a distance of no more than from p. To compute the overall cluster structure, the algorithm transitively ....

....with 16 dimensional feature vectors extracted from geometrical parts and variants thereof. The Euclidean distance was used for the similarity join. We determined the distance parameters for each data set such that they are suitable for clustering following the selection criteria proposed in [SEKX 98] Figure 10 shows our experiments using uniformly distributed 8 dimensional point data. In the left diagram, the database size is varied from 0.5 million to 40 million points while on the right side results are compared for varying values of the =parameter. The largest database was about 1.2 ....

Sander J., Ester M., Kriegel H.-P., Xu X.: Density -Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Vol. 2, No. 2, 1998.

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J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Minining and Knowledge Discovery, 2(2):169--194, 1998.

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M. Ester, H.P. Kriegel, J. Sander and X. Xu, Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications, Data Mining and Knowledge Discovery, Volume 2 , Issue 2, 1998

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Jorg Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Min. Knowl. Discov., 2(2):169--194, 1998.

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J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2):169--194, 1998.

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J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2):169--194, 1998.

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J. Sander, M. Ester, H.-P. Kriegel, and X. Xu, Density-based clustering in spatial databases: The algorithm gdbscan and its applications, Data Mining and Knowledge Discovery 2 (1998), no. 2, 169--194.

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Sander, J., Ester, M., Kriegel, H.-P. & Xu, X. [1998] "Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications", Data Mining and Knowledge Discovery, 2(2), 169--194.

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SANDER J. etc. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. In Data Mining and Knowledge Discovery 2, 2, 169-194.

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Jorg Sander, Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Min. Knowl. Discov., 2(2):169--194, 1998.

No context found.

J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2:169--194, 1998.

No context found.

J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. "Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications". Data Mining and Knowledge Discovery, 2:169--194, 1998.

No context found.

J. Sander, M. Ester, H.-P. Kriegel, and X. Xu, "Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 169--194, 1998.

No context found.

J. Sander, M. Ester, H.-P. Kriegel and X. Xu. DensityBased Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, 2(2), pp.169--194, 1998.

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J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2):169--194, 1998.

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