Wei Song
Qian Qiao
ABSTRACT
Mining high utility co-location patterns (HUCP) is a promising technique in spatial data mining because it treats different features with different levels of importance. Existing HUCP mining (HUCPM) algorithms are based on row instances and table instances, leading to high computational cost. To overcome this problem, an HUCPM algorithm based on the subsume index (HUCPM-SI) is developed using a typical high utility itemset mining model. Maximal cliques are first discovered, enabling the spatial database to be transformed into a clique transaction database. The subsume index is then used to mine HUCPs. Tests conducted on synthetic and real datasets demonstrate the advantages of the HUCPM-SI algorithm.
- Andrzejewski, W., and Boinski, P. 2019. Parallel approach to incremental co-location pattern mining. Inform. Sciences 496 (2019)485--505Google Scholar
- Han, J., Pei, J., Yin, Y., and Mao, R. 2004. Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl. Discov. 8, 1 (2004) 53--87.Google ScholarDigital Library
- Huang, Y., Shekhar, S., and Xiong, H. 2004. Discovering colocation patterns from spatial data sets: a general approach. IEEE T. Knowl. Data En. 16, 12 (2004) 1472--1485.Google ScholarDigital Library
- Huang, Y., Zhang, L., and Yu, P. 2005. Can we apply projection based frequent pattern mining paradigm to spatial co-location mining? In Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD '05). Springer, 719--725.Google Scholar
- Kim, S. K., Kim, Y., and Kim, U. 2011. Maximal cliques generating algorithm for spatial co-location pattern mining. In Proceedings of the FTRA International Conference on Secure and Trust Computing, Data Management, and Application (STA'11). Springer, 241--250.Google Scholar
- Liu, Y., Liao, W.-K., and Choudhary, A. N. 2005. A two-phase algorithm for fast discovery of high utility itemsets. In Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD '05). Springer, 689--695.Google Scholar
- Son, L. H., Lanzi, P. L., Cuong, B. C. and Hung, H. A. 2012. Data mining in GIS: a novel context-based fuzzy geographically weighted clustering algorithm. Intl. J. Mach. Learn. Comput. 2, 3 (2012) 235--238.Google ScholarCross Ref
- Song, W., Yang, B., and Xu, Z. 2008. Index-BitTableFI: An improved algorithm for mining frequent itemsets. Knowl.-Based Syst. 21, 6 (2008) 507--513.Google ScholarDigital Library
- Song, W., Zhang, Z., and Li, J. 2016. A high utility itemset mining algorithm based on subsume index. Knowl. Inf. Syst. 49, 1 (2016) 315--340.Google ScholarDigital Library
- Wang, L., Jiang, W., Chen, H., and Fang Y. 2017. Efficiently mining high utility co-location patterns from spatial data sets with instance-specific utilities. In Proceedings of the 22nd International Conference on Database Systems for Advanced Applications (DASFAA'17). Springer, 458--474.Google Scholar
- Wang, S., and Yuan, H. 2014. Spatial data mining: a perspective of big data. Int. J. Data Warehous. 10, 4 (2014) 50--70.Google ScholarCross Ref
- Wang, X., Wang, L., Lu, J., and Zhou, L. 2016. Effectively updating high utility co-location patterns in evolving spatial databases. In Proceedings of the 17th International Conference on Web-Age Information Management (WAIM'16). Springer, 67--81.Google Scholar
- Yang, S., Wang, L., Bao, X., and Lu, J. 2015. A framework for mining spatial high utility co-location patterns. In Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '15). IEEE, 595--601.Google Scholar
- Yoo, J. S., and Bow, M. 2012. Mining spatial colocation patterns: a different framework. Data Min. Knowl. Disc. 24, 1 (2012) 159--194.Google ScholarDigital Library
Index Terms
- Mining High Utility Co-location Patterns Using the Maximum Clique and the Subsume Index
Recommendations
A high utility itemset mining algorithm based on subsume index
High utility itemset mining addresses the limitations of frequent itemset mining by introducing measures of interestingness that reflect the significance of an itemset beyond its frequency of occurrence. Among such algorithms, level-wise candidate ...
Incrementally mining high utility patterns based on pre-large concept
In traditional association rule mining, most algorithms are designed to discover frequent itemsets from a binary database. Utility mining was thus proposed to measure the utility values of purchased items for revealing high utility itemsets from a ...
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number ...
Comments