Focusing on the issue that the existing co-location pattern mining algorithms fail to effectively address the problem of unevenly distributed spatial objects, a hierarchical co-location pattern mining approach of unevenly distributed fuzzy spatial objects was proposed. Firstly, an unevenly distributed dataset generation method was put forward. Secondly, the unevenly distributed dataset was partitioned by a hierarchical mining method in order to provide each region with an even spatial distribution. Finally, the spatial data mining of the separated fuzzy objects was conducted by means of the improved PO_RI_PC algorithm. Based on the distance variation coefficient, the neighborhood relationship graph for each sub-region was constructed to complete the regional fusion, and then the co-location pattern mining was realized. The experimental results show that, compared to the traditional method, the proposed method has higher execution efficiency. With the change of the number of instances and uneven degree, more co-location sets are mined, and the average increase reaches about 25% under the same condition, more accurate mining results are obtained through this method.

In the field of data mining, high utility itemset mining has been widely studied. However, high utility itemset mining does not consider the effect of the itemset length. To address this issue, high average-utility itemset mining has been proposed. At present, the proposed high average utility itemset mining algorithms take a lot of time to dig out the high average-utility itemset. To solve this problem, an improved high average itemset mining algorithm, named FHAUI (Fast High Average Utility Itemset), was proposed. FHAUI stored the utility information in the utility-list and mined all the high average-utility itemsets from the utility-list structure. At the same time, FHAUI adopted a two-dimensional matrix to effectively reduce the number of join-operations. Finally, the experimental results on several classical datasets show that FHAUI has greatly reduced the number of join-operations, and reduced its cost in time consumption.

Along with the development of computer technology , traditional computer forensics model could not meet the requirements for safety. The new forensic model was proposed here. Camparing with traditional computer forensic model, the major differenced between these two models lies on the distributed structure and the mechanism of dynamical data gathering. With this two characteristics, forensics system based on the new model could gather real-time evidences dynamically in a distributed system, and save this evidences in a safe place in time. So unauthorised deletion ,change to evidences could be detected and prevented. Then the stored evidences could be used for further analysis and review.