@article {323,
	title = {CMCCR: Classification based on multiple class-correlation rules},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {64 - 70},
	abstract = {Previous studies show that associative classification achieves higher classification accuracy than traditional classification approaches. However, associative classification suffers from two major deficiencies and thus sometimes has lower classification accuracy. First, it often generates a large number of rules when the minimum support is set to be low. It is not only time consuming to discover so many itemsets, but also very difficult to select high quality rules from a huge set of rules. Second, the association measure confidence does not reflect correlation relationships between itemsets and the class label. To deal these problems, in this paper, we propose a new classification approach called classification based on multiple class-correlation rules (CMCCR). First, we use both support and all-confidence to mine not only frequent but also mutually associated itemsets. This method not only saves the running time but as well considerably reduces the number of itemsets generated, and thus sharply decreases the number of class-correlation rules produced. Second, we use a new correlation measure correlation confidence to discover class-correlation rules. The measure correlation confidence has two bounds:- 1and 1. It is easy to control the correlation degree of the class-correlation rules found. Finally, we use multiple class-correlation rules and average correlation degree to measure the combined effect of group rules. Experimental results on the mushroom data set show that CMCCR has higher and more stable accuracy than associative classification and decision tree method.},
	keywords = {All-confidence, Class-correlation rule, Classification, Correlation confidence},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866448185\&partnerID=40\&md5=10fb4c8ff12d02f2555fb4ba4c8f5d04},
	author = {Zhou, Z. and Wang, X. and Pan, G.}
}
@article {349,
	title = {An intelligent communication path planning method of metallurgical equipment multi-dimensional information space},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2012},
	pages = {295 - 300},
	abstract = {With the development of ubiquitous information environment in metallurgical industry, driving type of metallurgical equipment systems is changing from energy-driven to information-driven. The problems of the metallurgical equipment information communication path intelligent planning are taken as the research object. Based on the cellular automata theory, the integrated framework of metallurgical equipment information space is built. Information communication path intelligent planning method of metallurgical equipment based on ant colony algorithm is explored. The practicality and effectiveness of the divided information spaces and designed algorithms are verified by simulation, which provides theoretical support to enhance the level of metallurgical equipment.},
	keywords = {Ant colony algorithm, Cellular automata, Intelligent planning of the communication path, Metallurgical equipments},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870775516\&partnerID=40\&md5=662c43d3290cf4324b438895204b0c6b},
	author = {Liu, J. and Kong, J. and Zhou, M. and Wang, X.}
}
@article {248,
	title = {A novel access control strategy for distributed data systems},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2010},
	pages = {291 - 297},
	abstract = {It is one of the most important challenges to balance between security and scalability in large distributed data systems. In this paper, we introduce a new data distribution model, which is a generalized tree structure called as multitree. And its access control strategy is investigated. In our multitree data model, the database schema is expressed as a schema graph, and a database instance is imagined as a data graph. It is different from the traditional hierarchical data model, since a node in our multitree model can have many parent nodes. All the data graphs or schema are transformed into multitrees. The complex data relation of the distributed data systems is reduced based on graph theory. The complexity of distribution is decreased significantly. In the multitree model, each user has a maximum access range corresponding to its multitree. It is integrated naturally with security. We use organization structure to bound the data range that a user can access, and use roles to restrict the operations that the user can perform. The scalability of data distribution and access control administration are evaluated through the instance adapted from the TPC-C database. The results illustrates our data distribution model is helpful for the system to be resilient and scalable. It is suitable for large distributed system and cloud relational database.},
	keywords = {Access control, Cloud database, Data distribution, Multi-hierarchical model, Multitree},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960669952\&partnerID=40\&md5=82211ced06d718d2f20daa5dfc146798},
	author = {Zhang, J. and Wang, X. and Liu, H. and Meng, J.}
}
