A fast space-saving algorithm for maximal co-location pattern mining
- 1 November 2016
- journal article
- research article
- Published by Elsevier BV in Expert Systems with Applications
- Vol. 63, 310-323
- https://doi.org/10.1016/j.eswa.2016.07.007
Abstract
No abstract availableKeywords
Funding Information
- Chinese Academy of Sciences (Y6SJ2800CX)
This publication has 15 references indexed in Scilit:
- Local and global spatio-temporal entropy indices based on distance-ratios and co-occurrences distributionsInternational Journal of Geographical Information Science, 2014
- Spatially enabled emergency event analysis using a multi-level association rule mining methodNatural Hazards, 2013
- Exploratory analysis of the interrelations between co-located boolean spatial features using network graphsInternational Journal of Geographical Information Science, 2012
- Mining qualitative patterns in spatial cluster analysisExpert Systems with Applications, 2012
- An order-clique-based approach for mining maximal co-locationsInformation Sciences, 2009
- A note on the problem of reporting maximal cliquesTheoretical Computer Science, 2008
- ON THE RELATIONSHIPS BETWEEN CLUSTERING AND SPATIAL CO-LOCATION PATTERN MININGInternational Journal on Artificial Intelligence Tools, 2008
- The worst-case time complexity for generating all maximal cliques and computational experimentsTheoretical Computer Science, 2006
- Discovering colocation patterns from spatial data sets: a general approachIEEE Transactions on Knowledge and Data Engineering, 2004
- Algorithm 457: finding all cliques of an undirected graphCommunications of the ACM, 1973