Conditional Pattern Bases Research Articles

Research on the Correlation between Multisource Big Data Virtual Assisted Preschool Education and the Development of Children's Innovative Ability.

Objective Innovation ability is an important part of children's core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children's innovative ability. Method In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children's innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children's innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children's innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children's innovation ability.

Towards Enhancing the Performance of Parallel FP-Growth on Spark

Frequent itemset mining (FIM) is a crucial tool for identifying hidden patterns in information. FP-Growth is an FIM algorithm used to find associations. When the data size increases, the execution of FIM algorithms on a single machine suffers from computational problems, such as memory and time consumption. For these reasons, parallel and distributed processing on platforms such as Spark is essential. The parallel frequent pattern (PFP) is the implementation of FP-Growth in Spark. The main problem with PFP is that it does not consider the load balancing between cluster units. This research proposes an enhanced balanced parallel frequent pattern “EBPFP” algorithm to enhance and balance the PFP. The proposed algorithm (EBPFP) proposes two ideas. First, a strategy for load balancing between groups is proposed to ensure that the items are evenly divided between the nodes, and the cluster resources are used more effectively. Second, the improved conditional pattern base (ICPB) method aims to remove infrequent items from the conditional pattern base before constructing local FP-Trees. The experimental results show that the proposed EBPFP algorithm outperforms PFP, and the difference in running time between EBPFP and PFP was 21.56% and 39.72%, respectively.

Mining algorithm for weighted FP-tree frequent item sets based on two-dimensional table

FP-growth algorithm is a classic algorithm of mining frequent item sets, but there exist certain disadvantages for mining the weighted frequent item sets. Based on the weighted downward closure property of the weighted model, this paper proposed that the weighted support was recorded in the two-dimensional table. This method has saved the process to search for the first conditional pattern base by traversing the weighted FP-tree, improved the efficiency of the weighted frequent item sets generated.

An Efficient IFP - Tree Based High Utility Pattern Mining of Itemsets with Indexing

Conventional methods of Association rule mining and Frequent Itemset Mining (FIM) cannot satisfy the anxieties emerging from certain real applications. In real world, for making some decisions the user wants to know the total profit grossed by an itemset or item. To evaluate this it needs to take into account the quantity of the purchased item. The profit of an item considers the gain of single item and the number of item purchased. To address these, utility mining has been introduced. In this the utility of an itemset is calculated as the number of item purchased and the product of the gain of the item. Utility mining concentrates on both the reputation of an item in the knowledge base (i.e.) profit or exterior utility and the reputation of an item in the transaction (i.e.) quantity or interior utility of an item. In this study, a novel Improved frequent-pattern tree (IFP-tree) structure, which is an extended prefix-tree structure for storing crucial information about frequent patterns, and develop an efficient IFP-tree-based mining method based on the generation of conditional utility pattern base which leads to the conditional utility IFP-tree for mining the complete set of frequent patterns. As the item set in the sanitized database and original database are segmented in different areas, the itemset kept in the different areas are indexed through candidate keys for increasing the access speed and fast retrieval of data. This process will increase the accuracy of the database and it preserves the sensitive data items for a longer time. The assessment report shows that the generation of less candidate patterns makes algorithms to run faster.

A non-group parallel frequent pattern mining algorithm based on conditional patterns

Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.

HUPT-mine: an efficient algorithm for high utility pattern mining

In recent years, the problem of high utility pattern mining becomes one of the most important research areas in data mining. High utility pattern mining extracts patterns which have utility value higher than or equal to user specified minimum utility. The problem is challenging, because of the non-applicability of anti-monotone property of frequent pattern mining. The existing high utility pattern mining algorithm adopts level wise candidate generation and many recently proposed approaches also generate large number of candidate itemsets. In this paper, a novel high utility pattern tree (HUPT) is proposed by applying two pruning strategies to reduce number of candidate itemsets by scanning database twice. For each conditional pattern base, a local tree is constructed with required information to generate candidate itemsets, by employing pattern growth approach. The experimental results on different datasets show that it reduces the number of candidate itemsets and also outperforms two-phase algorithm for dense datasets with long transactions.

No-header-table FP-Growth algorithm

Concerning the problem of low traversal efficiency when searching the FP-tree for conditional pattern bases,a new no-header-table FP-Growth algorithm was proposed.The algorithm employed a recursively backtracking way to search for conditional pattern bases,avoiding traversing the same FP-tree path multiple times.Compared with FP-Growth algorithm in terms of theoretical analysis and actual mining performance,this algorithm greatly reduced the searching cost and improved the mining efficiency of frequent patterns by 2-5 times.Finally,the algorithm was used to mine association rules in telecommunication network alarms.The high mining speed,with the coverage of 83.3% against correct rules,shows that it is superior to FP-Growth both in time and space performance.

Research and application on Web information retrieval based on improved FP-growth algorithm

A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.