〔1〕 HUANG Y, SHEKHAR S, XIONG H. Discovering Colocation
Patterns from Spatial Data Sets: A General Approach〔J〕. IEEE
Transactions on Knowledge and Data Engineering, 2004, 16(12): 1472-1485.
〔2〕 YOO J S, SHEKHAR S, SMITH J, et al. A Partial Join Approach for
Mining Co-Location Patterns〔C〕//Proceedings
of the 12th Annual ACM International Workshop on Geographic Information
Systems-GIS '04. November 12-13, 2004. Washington DC, USA. New York: ACM Press, 2004: 241-249.
〔3〕 YOO J S, SHEKHAR S. A Joinless Approach for
Mining Spatial Colocation Patterns〔J〕. IEEE
Transaction on Kno-wledge and Data Engineering, 2006, 18(10): 1323-1337.
〔4〕 杨世晟, 王丽珍, 芦俊丽,等. 空间高效用Co-location模式挖掘技术初探〔J〕. 小型微型计算机系统, 2014, 35(10): 2302-2307.
〔5〕 WANG L Z, BAO Y Z, LU J, et al. A New Join-Less Approach for Co-Location Pattern Mining〔C〕//2008 8th IEEE International Conference on Computer and Information
Technology. July 8-11, 2008, Sydney, NSW, Australia. IEEE, 2008: 197-202.
〔6〕 WANG L Z, HAN J, CHEN H M, et al. Top-k Probabilistic
Prevalent Co-Location Mining in Spatially Uncertain Data Sets〔J〕. Frontiers of Computer Science, 2016, 10(3): 488-503.
〔7〕 YOO J S, VASUDEVAN H. Effectively Updating
Co-Location Patterns in Evolving Spatial Databases〔C〕//2014 the Sixth International Conferences on Pervasive Patterns and
Applications, 2014: 96-99.
〔8〕 LU J L, WANG L Z, FANG Y, et al. Mining Strong Symbiotic
Patterns Hidden in Spatial Prevalent Co-Location Patterns〔J〕. Knowledge-Based Systems, 2018, 146: 190-202.
〔9〕 芦俊丽, 王丽珍, 赵家松, 等. 从动态空间数据库中挖掘共生关系和竞争关系〔J〕. 南京大学学报(自然科学版), 2018, 54(2):436-451.
〔10〕 芦俊丽, 王丽珍, 肖清, 等. 空间co-location模式增量挖掘及演化分析〔J〕. 软件学报, 2014, 25(2):189-200.
〔11〕 WANG X X, WANG L Z, LU J L, et al. Effectively Updating High
Utility Co-Location Patterns in Evolving Spatial Databases〔C〕// International Conference on Web-age Information Management.
Springer, Cham, 2016, DOI:10.1007/978-3-319-39937-9_6.
〔12〕 WANG L Z, JIANG W G, CHEN H M, et al. Efficiently Mining High
Utility Co-Location Patterns from Spatial Data Sets with Instance-Specific
Utilities〔C〕// International Conference on
Database Systems for Advanced Applications. Cham: Springer International Publishing, 2017: 458-474.
〔13〕 王晓璇, 王丽珍, 陈红梅, 等. 基于特征效用参与率的空间高效用co-location模式挖掘方法〔J〕. 计算机学报, 2019, 42(8): 1721-1738.
〔14〕 ZENG X, YANG J, LI Z P, et al. A Method of Mining Spatial
High Utility Co-Location Patterns Based on Feature Actual Participation Weight〔C〕// IOP Conf. Series: Journal of Physics: Conf. Series. 2019, 1168: 032064.
〔15〕 HUANG Y, PEI J, XIONG H. Mining Co-Location
Patterns with Rare Events from Spatial Data Sets〔J〕.
GeoInformatica, 2006, 10(3): 239-260.
〔16〕 冯岭, 王丽珍, 高世健. 一种带稀有特征的空间co-location模式挖掘新方法〔J〕. 南京大学学报(自然科学版), 2012, 48(1): 99-107.
〔17〕 LIN J C W, LI T, FOURNIER-VIGER P, et al. An Efficient Algorithm to Mine High Average-Utility Itemsets〔J〕. Advanced Engineering Informatics, 2016, 30(2):233-243.
〔18〕 LIN J C W, REN S F, FOURNIER-VIGER P, et al. EHAUPM: Efficient High Average-Utility
Pattern Mining with Tighter Upper Bounds〔J〕. IEEE Access, 2017, 5: 12927-12940.
〔19〕 WU J M T, LIN J C W, PIROUZ M, et al. TUB-HAUPM: Tighter Upper Bound for Mining
High Average-Utility Patterns〔J〕. IEEE Access, 2018, 6: 18655-18669.
〔20〕 TRUONG T, DUONG H, LE B, et al. Efficient Vertical Mining of High Average-Utility Itemsets
Based on Novel Upper-Bounds〔J〕. IEEE
Transactions on Knowledge and Data Engineering, 2019, 31(2): 301-314.
|