TY - GEN
T1 - ESTATE
T2 - 13th International Conference on Discovery Science, DS 2010
AU - Stepinski, Tomasz F.
AU - Salazar, Josue
AU - Ding, Wei
AU - White, Denis
PY - 2010
Y1 - 2010
N2 - We propose an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. Proposed strategy, ESTATE (Exploring Spatial daTa Association patTErns), inverts such classification by interpreting different classes found in the dataset in terms of sets of discriminative patterns of its attributes. It consists of several core steps including discriminative data mining, similarity between transactional patterns, and visualization. An algorithm for calculating similarity measure between patterns is the major original contribution that facilitates summarization of discovered information and makes the entire framework practical for real life applications. Detailed description of the ESTATE framework is followed by its application to the domain of ecology using a dataset that fuses the information on geographical distribution of biodiversity of bird species across the contiguous United States with distributions of 32 environmental variables across the same area.
AB - We propose an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. Proposed strategy, ESTATE (Exploring Spatial daTa Association patTErns), inverts such classification by interpreting different classes found in the dataset in terms of sets of discriminative patterns of its attributes. It consists of several core steps including discriminative data mining, similarity between transactional patterns, and visualization. An algorithm for calculating similarity measure between patterns is the major original contribution that facilitates summarization of discovered information and makes the entire framework practical for real life applications. Detailed description of the ESTATE framework is followed by its application to the domain of ecology using a dataset that fuses the information on geographical distribution of biodiversity of bird species across the contiguous United States with distributions of 32 environmental variables across the same area.
KW - association patterns
KW - biodiversity
KW - clustering
KW - similarity measure
KW - Spatial databases
UR - https://www.scopus.com/pages/publications/78650107512
UR - https://www.scopus.com/pages/publications/78650107512#tab=citedBy
U2 - 10.1007/978-3-642-16184-1_23
DO - 10.1007/978-3-642-16184-1_23
M3 - Conference contribution
AN - SCOPUS:78650107512
SN - 3642161839
SN - 9783642161834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 326
EP - 340
BT - Discovery Science - 13th International Conference, DS 2010, Proceedings
Y2 - 6 October 2010 through 8 October 2010
ER -