FINDING CLUSTERS IN SPATIAL DATA
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University of Cincinnati / OhioLINK
2007
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin11795213372021-08-03T06:11:55Z FINDING CLUSTERS IN SPATIAL DATA SHENCOTTAH K.N., KALYANKUMAR Computer Science clusters Spatial data mining Quad-Tree Spatial clustering Spatial data mining is the discovery of patterns in spatial databases. The driving factor for research in spatial data mining is the increase in collection of spatial data through business and geographical database systems. Some of the spatial data collected include remotely sensed images, geographical information with spatial attributes such as location, digital sky survey data, mobile phone usage data, and medical data. Spatial data mining takes into account spatial and non-spatial data attributes in these data. The goal of our research is to discover and merge cluster regions which contain spatial data values in a given standard deviation range. Our approach involves visualizing the data in a 2-D grid, and involves mining the data using quad-tree, a spatial data structure. Quad-tree stores the entire 2-D grid in its leaf node at level k. Cluster information such as standard deviation, mean, x-coordinate, y-coordinate, and number of nodes are calculated at k-1 level, and synthesized up the quad-tree. Based on the input standard deviation range, we discover clusters, determine adjacency of the clusters and merge interesting clusters. By increasing or decreasing the input standard deviation range, we observe that the cluster boundary changes and we discover clusters of different shapes such as rectangle, triangle, rhombus and concave. Our algorithm can be useful in identifying patterns such as increase or decrease in crime rate or spread of disease in a given region. The 2-D grid refers to the physical space, and the values in the cells refers to non-spatial attribute values such as crime, temperature etc. Spatial abstractions such as region, location etc. are a result of clustering of non-spatial attribute values. 2007-07-03 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
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NDLTD |
| topic |
Computer Science clusters Spatial data mining Quad-Tree Spatial clustering |
| spellingShingle |
Computer Science clusters Spatial data mining Quad-Tree Spatial clustering SHENCOTTAH K.N., KALYANKUMAR FINDING CLUSTERS IN SPATIAL DATA |
| author |
SHENCOTTAH K.N., KALYANKUMAR |
| author_facet |
SHENCOTTAH K.N., KALYANKUMAR |
| author_sort |
SHENCOTTAH K.N., KALYANKUMAR |
| title |
FINDING CLUSTERS IN SPATIAL DATA |
| title_short |
FINDING CLUSTERS IN SPATIAL DATA |
| title_full |
FINDING CLUSTERS IN SPATIAL DATA |
| title_fullStr |
FINDING CLUSTERS IN SPATIAL DATA |
| title_full_unstemmed |
FINDING CLUSTERS IN SPATIAL DATA |
| title_sort |
finding clusters in spatial data |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2007 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179521337 |
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AT shencottahknkalyankumar findingclustersinspatialdata |
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1719432596766588928 |