Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Algorithmic Foundations of Geographic Information Systems, this book originated from the CISM Advanced School on the Algorithmic Foundations of Geographic Information Systems
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Intelligent clustering with instance-level constraints
Intelligent clustering with instance-level constraints
Computers and Electronics in Agriculture
Determination of management zones in corn (Zea mays L.) based on soil fertility
Computers and Electronics in Agriculture
Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)
International Journal of Geographical Information Science
Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity
Computers and Electronics in Agriculture
Data mining in precision agriculture: management of spatial information
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Regression models for spatial data: an example from precision agriculture
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Spatial variable importance assessment for yield prediction in precision agriculture
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Machine Learning
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Precision Agriculture has become an emerging topic over the last ten years. It is concerned with the integration of information technology into agricultural processes. This is especially true for the ongoing and growing data collection in agriculture. Novel ground-based sensors, aerial and satellite imagery as well as soil sampling provide large georeferenced data sets with high spatial resolution. However, these data lead to the data mining problem of finding novel and useful information in these data sets. One of the key tasks in the area of precision agriculture is management zone delineation: given a data set of georeferenced data records with high spatial resolution, we would like to discover spatially mostly contiguous zones on the field which exhibit similar characteristics within the zones and different characteristics between zones. From a data mining point of view, this task comes down to a variant of spatial clustering with a constraint of keeping the resulting clusters spatially mostly contiguous. This article presents a novel approach tailored to the specifics of the available data, which do not allow for using an existing algorithm. A variant of hierarchical agglomerative clustering will be presented, in conjunction with a spatial constraint. Results on available multi-variate data sets and subsets will be presented.