Parallel Approach For Finding Co-Location Patterns - Martha, Sheshikala
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The book discuss about the spatial co-location mining which finds the co-locations parallel which reduces the time complexity. Spatial co-location patterns represent a subset of features whose instances are frequently co-located in close proximity; For example Mountain area and new truck purchased are frequently co-located patterns, indicating that a person living close to mountainous areas is likely to buy a truck. Since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships the implementation of co-location mining can be taken as a challenge.…mehr

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Produktbeschreibung
The book discuss about the spatial co-location mining which finds the co-locations parallel which reduces the time complexity. Spatial co-location patterns represent a subset of features whose instances are frequently co-located in close proximity; For example Mountain area and new truck purchased are frequently co-located patterns, indicating that a person living close to mountainous areas is likely to buy a truck. Since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships the implementation of co-location mining can be taken as a challenge.
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Autorenporträt
Dr. M. Sheshikala completed Ph.D from KLEF, Vijayawada under the Guidance of Dr.D.Rajeswara Rao in the Department of CSE, and having teaching experience of 13+years and presently working in SR Engineering College, Warangal, India.