Association rule mining algorithms can be used to discover all item associations (or rules) in a dataset that satisfy user-specified constraints, e.g., minimum supports (minsup) and minimum confidence (minconf). Since only one minsup is used for the whole database, it is implicitly assumed in the model that all items are of the same nature and/ or have similar frequencies in the data. This rarely applies in reality, so, based on an FP-Tree, a new algorithm is proposed called multiple minimum supports for discovering maximum frequent item sets algorithm (MSDMFIA). The algorithm allows users to specify multiple minsups to reflect item natures and various frequencies, which resolves bottlenecks in traditional algorithms, e.g., the frequent generation of candidate itemsets and database scanning. Experimental results show that functionality and performance of the proposed algorithm is significantly improved compared with most others.

In order to improve the discriminating capacity of the RFID tags for agricultural products traceability data, a new handling engine ORFID-CEP for complex events of agricultural products RFID Tag based on ontology, is proposed by introducing the event ontology model into the field of the agricultural products quality safety administration. In the new engine, the ontology model of RFID event, semantic space and the rules of the event ontology are defined. In the RFID practical application instances, the number of the fringe real-time events is so large that those methods used in current systems can not handle them in time. Thus, many significant events are lost. Aiming to overcome the problem, the transform system of work flow for the complex event ontology and the optimized strategies for event exploration, event operation and restrictive conditions of event appearance are established. Experimental results show that the new engine can mine more complex events with semantic information than the conventional Esper-CEP engine. Additionally, the new engine is steadier than the Esper-CEP engine. With increasing number of the events, the growth rate of the new engine for mining complex events raises more quickly than Esper-CEP, which indicates that the engine has the good ability of information process.

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