Linked Life Data

19022723

Source:http://linkedlifedata.com/resource/pubmed/id/19022723

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2008-11-21
pubmed:abstractText
Association rule mining, originally proposed for market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Extracting interesting patterns and rules from spatial data sets, composed of images and associated ground data, can be of importance in precision agriculture, resource discovery, and other areas. However, in most cases, the sizes of the spatial data sets are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an efficient approach to derive association rules from spatial data using Peano Count Tree (P-tree) structure. P-tree structure provides a lossless and compressed representation of spatial data. Based on P-trees, an efficient association rule mining algorithm PARM with fast support calculation and significant pruning techniques is introduced to improve the efficiency of the rule mining process. The P-tree based Association Rule Mining (PARM) algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1941-0492
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1513-24
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
PARM--an efficient algorithm to mine association rules from spatial data.
pubmed:affiliation
Department of Computer Science, East Carolina University, Greenville, NC 27858-4353, USA. dingq@ecu.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.