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## Wavecluster: A multi-resolution clustering approach for very large spatial databases (1998)

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### Other Repositories/Bibliography

Citations: | 221 - 6 self |

### Citations

3532 | A theory for multiresolution signal decomposition: The wavelet representation - Mallat - 1989 |

2228 | Finding groups in data: an introduction to cluster analysis, volume 344. Wiley-Interscience - Kaufman, Rousseeuw - 2009 |

1785 | A Density-Based Algorithm for Discovering Clusters
- Ester, Kriege, et al.
- 1996
(Show Context)
Citation Context ...method impractical for very large databases. Ester et al presented a clustering algorithm DBSCAN relying on a density-based notion of clusters. It is designed to discover clusters of arbitrary shapes =-=[EKSX96]-=-. The key idea in DBSCAN is that for each point of a cluster, the neighborhood of a given radius has to contain at least a minimum number of points, i.e. the density in the neighborhood has to exceed ... |

1717 |
Robot Vision
- Horn
- 1986
(Show Context)
Citation Context ...n finding clusters at different levels of details. For example, as shown in Figure 5, for a 2-dimensional feature space, the subbands LL show the clusters at different scales. We use the algorithm in =-=[Hor88]-=- to find the connected components in the 2-dimensional feature space (image) . The same concept can be generalized for higher dimensions. Figure 12 in Section 5, shows the clusters that WaveCluster fo... |

1180 | Multirate Systems and Filter Banks. - Vaidyanathan - 1993 |

709 | Efficient and effective clustering methods for spatial data min ing - Ng, Han - 1994 |

626 |
Multi-resolution approximations and wavelet orthonormal bases of L2(R
- Mallat
- 1989
(Show Context)
Citation Context ...ering very large datasets takes only a few seconds. Using parallel processing we can get even faster responses. Applying wavelet transform on a signal decomposes it into different frequency sub-bands =-=[Mal89a]-=-. We now briefly review wavelet-based multi-resolution decomposition. More details can be found in Mallat's paper [Mal89b]. To have multi-resolution representation of signals we can use discrete wavel... |

290 | STING: A Statistical Information Grid Approach to Spatial Data Mining”, - Wang, Yang, et al. - 1997 |

263 |
an e cient data clustering method for very large databases
- BIRCH
- 1996
(Show Context)
Citation Context ...hich is an obvious advantage over partitioning algorithms. The disadvantage is that the termination condition has to be specified. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) =-=[ZRL96]-=- uses a hierarchical data structure called CF-tree for incrementally and dynamically clustering the incoming data points. CF-tree is a height balanced tree which stores the clustering features. BIRCH ... |

236 | Pattern Recognition: Statistical, Structural, and Neural Approaches, - Schalkoff - 1992 |

137 | Transform features for texture classification and discrimination in large image databases, in - Smith, Chang - 1994 |

137 | An O(1og n) parallel connectivity algorithm,” - Shiloach, Vishkin - 1982 |

70 | Nearest neighbor clutter removal for estimating features in spatial point processes. - Byers, Raftery - 1998 |

58 | Compressing Still and Moving Images with Wavelets
- Hilton, Jawerth, et al.
- 1994
(Show Context)
Citation Context ...band and low frequency subband). The wavelet model can be generalized to n-dimensional signals in which onedimensional transform can be applied multiple times. Methods have been used to compress data =-=[HJS94]-=-, or to extract features from signals (images) using wavelet transform [SC94, SZ97, SZB97]. The key idea in our proposed approach is to apply wavelet transform on the feature space (instead of the obj... |

35 | Finding Connected Components and Connected Ones on a Mesh-Connected Parallel Computer - Nassimi, Sahni - 1980 |

32 | Nonparametric maximum likelihood estimation of features in spatial point processes using Voronoi tessellation, - Allard, Fraley - 1997 |

22 | A (1997) An approach to clustering large visual databases using wavelet transform - Sheikholeslami, Zhang |

10 | Bultheel,“Wavelet transform using the lifting scheme”,Report - Uytterhoeven, Roose, et al. |

7 | Geographical Data Classification and Retrieval - Sheikholeslami, Zhang, et al. - 1997 |

5 | Gool. DOG-based unsupervized clustering for CBIR - Pauwels, Fiddelaers, et al. - 1997 |

1 | A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise - Hilton, Jawerth, et al. - 1994 |

1 | An O(logn) parallel connectivity algorithm - Wavelets, Banks - 1996 |