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Intrusion detection analyzes unauthorized accesses and malicious behaviors and finds intrusion behaviors and attempts by detecting the state and activity of an operation system to provide an effective means for intrusion defense. Applying the intrusion detection technology to databases is an effective method of enabling databases to have positive and active security mechanisms. This paper makes an...
An efficient algorithm to mine frequent item sets is crucial for mining association rules. Most of the previously used algorithms have generally been developed for using the computational time effectively, reducing the number of candidate itemsets and decreasing the number of scan in the database. However, the time can be reduced by aggregate transactions having similar itemsets. This paper, then...
Wireless sensor networks (WSNs) produce large scale of data in the form of streams. Recently, data mining techniques have received a great deal of attention in extracting knowledge from WSNs data. Mining association rules on the sensor data provides useful information for different applications. Even though there have been some efforts to address this issue in WSNs, they are not suitable when multiple...
Every element of the transaction in a transaction database may contain the components such as item number, quantity, cost of the item bought and some other relevant information of the customer. Most of the association rules mining algorithms to discover frequent itemsets do not consider the components such as quantity, cost etc. In a large database it is possible that even if the itemset appears in...
Efficiency is critical to data mining algorithm. Based on fully analyzing the PF_growth, an association rule mining algorithm, we in this paper give a new association rule mining algorithm called MFP. MFP algorithm converts a transaction database to an MFP_tree through scanning the transaction database only once, then prune the tree and at last mine the tree. Because the MFP algorithm scans a transaction...
We propose here an efficient data mining algorithm to hide collaborative recommendation association rules when the database is updated, i.e., when a new data set is added to the original database. For a given predicted item, a collaborative recommendation association rule set [10] is the smallest association rule set that makes the same recommendation as the entire association rule set by confidence...
Technology of frequent pattern tree is presented in the paper. This paper analyzes the defect and limitation of algorithm based on classic frequent pattern of association rules. Then based on KDD* model this article implement an association rules algorithms based on IFP-tree. The middle results and finally frequent patterns of the algorithm are stored on database. The algorithm in build IFP-tree and...
Most existing algorithms for mining frequent closed itemsets have to check whether a newly generated itemset is a frequent closed itemset by using the subset checking technique. To do this, a storing structure is required to keep all known frequent itemsets and candidates. It takes additional processing time and memory space for closure checking. To remedy this problem, an efficient approach called...
In this paper, we attempt to handle the maintenance of sequential patterns. New transactions may come from both the new customers and old customers. A fast updated sequential pattern tree (called FUSP-tree) structure is proposed to make the tree update process become easy. An incremental FUSP-tree maintenance algorithm is also proposed for reducing the execution time in reconstructing the tree. The...
Association rule mining is to find association relationships among large data sets. Mining frequent patterns is an important aspect in association rule mining. In this paper, an efficient algorithm named apriori-growth based on apriori algorithm and the FP-tree structure is presented to mine frequent patterns. The advantage of the apriori-growth algorithm is that it doesn't need to generate conditional...
Within the area of association rules mining, previous algorithms, e.g., FP-Growth and Apriori, have been generally accepted with high appraisals respectively. Most of these algorithms decompose the problem of mining association rules into two subproblems: find frequent pattern and generate the desired rules. Therefore, such a decomposition strategy cannot but bring delay problem when the size of database...
Spatial co-location and de-location patterns represent subsets of Boolean spatial feature types whose instances are often located in close/separate geographic proximity. Existing literatures pay more attention on mining colocation patterns based on distance threshold spatial relation. In this paper, we proposed a novel co-location and de-location patterns mining algorithm (CODEM) to discover useful...
Because of the low efficiency of Maximal Frequent Itemsets(MFI) updating methods, the MFI's updating methods were analyzed. A new algorithm UAMFI based on Full Merged Sorted FP-Tree (FMSFP-Tree) was proposed. By merging the Sorted FP-Tree and then obtaining the FMSFP-Tree, UAMFI uses the depth-first method to find and update MFI. Finally, the algorithm was tested on the mushroom and T15I4D100K database,...
Association rules mining is an important data mining task, this paper emphatically analyzes realization skill and defects of existing algorithms. On the basis, a novel measure, named statistical correlation, which can indicate the correlation degree of multi-items in a rule, is put forward to cut association rules with independent or negative correlation, and its concept, calculating formulas and...
Sequential pattern mining is an important data mining problem with broad application. Most of the previously developed sequential pattern mining methods need to scan the database many times. In this study, STMFP algorithm based on improved FP-tree is presented for sequential pattern mining. By improving the FP-tree structure, every node of the tree can store a set of items instead of one item. After...
In the past, the FUFP-tree maintenance algorithm is proposed to efficiently handle the association rules in incremental mining. In this paper, we attempt to modify the FUFP-tree maintenance algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases in incremental mining. A fast updated sequential pattern trees (FUSP...
In this paper, the structure of the prelarge tree is proposed to maintain association rules for record modification based on the concept of pre-large itemsets. Due to the properties of the pre-large concept, the proposed algorithm can achieve a good execution time for tree maintenance especially when each time a small number of records are modified. Experimental results show that the proposed prelarge-tree...
This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure...
FP-tree is an efficient algorithm for generating frequent item-sets and the present algorithms are all based on FP_tree generally. But the FP_treepsilas generative process needs much time and it needs to scan database twice. In order to improve the efficiency of constructing the FP_tree, a new fast algorithm called Level FP_tree (abbreviate L-FP_tree) was proposed. The algorithm contains two main...
Existing algorithms for mining maximal frequent itemsets have to do superset checking, and some of them using FP-tree have to construct conditional frequent pattern trees recursively. We present a novel algorithm for mining maximal frequent itemsets from a transactional database. In the algorithm, the FP-Tree data structure is used and adapted, and a new strategy called ldquoNBNrdquo (Node By Node)...
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