02257nas a2200205   4500008004100000245006800041210006600109260000900175300001200184490000700196520160100203653001901804653002701823653001901850653002701869100001301896700001301909700001201922856011701934       2012                        eng d00aCMCCR: Classification based on multiple class-correlation rules0 aCMCCR Classification based on multiple classcorrelation rules  c2012  a64 - 700 v103 aPrevious 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.10aAll-confidence10aClass-correlation rule10aClassification10aCorrelation confidence1 aZhou, Z.1 aWang, X.1 aPan, G.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866448185&partnerID=40&md5=10fb4c8ff12d02f2555fb4ba4c8f5d0401545nas a2200217   4500008004100000245011700041210006900158260000900227300001400236490000700250520077500257653002501032653002201057653005101079653002901130100001201159700001301171700001301184700001301197856011701210       2012                        eng d00aAn intelligent communication path planning method of metallurgical equipment multi-dimensional information space0 aintelligent communication path planning method of metallurgical   c2012  a295 - 3000 v103 aWith 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.10aAnt colony algorithm10aCellular automata10aIntelligent planning of the communication path10aMetallurgical equipments1 aLiu, J.1 aKong, J.1 aZhou, M.1 aWang, X.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870775516&partnerID=40&md5=662c43d3290cf4324b438895204b0c6b02070nas a2200229   4500008004100000245006500041210006300106260000900169300001400178490000600192520137000198653001901568653001901587653002201606653002901628653001401657100001401671700001301685700001201698700001301710856011701723       2010                        eng d00aA novel access control strategy for distributed data systems0 anovel access control strategy for distributed data systems  c2010  a291 - 2970 v83 aIt 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.10aAccess control10aCloud database10aData distribution10aMulti-hierarchical model10aMultitree1 aZhang, J.1 aWang, X.1 aLiu, H.1 aMeng, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960669952&partnerID=40&md5=82211ced06d718d2f20daa5dfc146798