Number Of Candidate Itemsets Research Articles

The Application of Big Data in the Management of Ideological and Political Education in the Development of Education Network

Based on a brief analysis of the current situation of university education management and research on intelligent algorithms, this article constructs a university education management system based on big data. For the clustering and prediction modules in higher education management, corresponding algorithms are used for optimization design. A fuzzy clustering algorithm based on entropy weight is proposed to address the shortcomings of the C-means clustering algorithm. This algorithm adds weighting coefficients on the basis of improvement and continuously updates the clustering centers. The prediction module uses Apriori algorithm to map and compress the target transaction database, reduces the number of candidate item sets through pruning process, and designs simulation experiments to measure the performance of the algorithm. The simulation results show that the clustering results of this algorithm are closer to the actual clustering situation, with shorter running time and better algorithm performance.

A Data Mining Algorithm for Association Rules with Chronic Disease Constraints.

The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China's medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved.

High average-utility itemset mining with multiple minimum utility threshold: A generalized approach

High Average-Utility Itemset (HAUI) mining is an emerging pattern mining technique to extract meaningful patterns from a transaction dataset. In the past, several HAUI mining algorithms have been developed with efficient upper-bounds and pruning strategies. However, all these algorithms use a single value of the minimum average-utility threshold for all the itemsets, which limits their applicability to real-life datasets. In order to address this issue, several HAUI mining algorithms with multiple average-utility thresholds have been developed that process the items in ascending order of their minimum average-utility threshold. However, it makes them inapplicable on traditional HAUI mining algorithms. Moreover, the perturbation in preference of items may reduce the performance of the algorithms. This paper presents an HAUI mining algorithm named Generalized High Average-utility Itemset Miner (GHAIM) that processes the items in ascending order of their Average Utility Upper-Bound (AUUB) like the traditional HAUI mining algorithms. A new approach named suffix minimum average-utility is proposed to retain the downward closure property of AUUB and several pruning methods. Besides, a compact list structure is also proposed to mine the HAUIs in one phase. Several pruning methods have been introduced for reducing search space and improving efficiency. Extensive experiments were performed with different sparse and dense types of datasets to determine GHAIM efficiency compared to two existing algorithms. It was observed from the results that GHAIM outperforms both the current algorithms in run time, memory consumption, number of candidate itemsets, and scalability.

Parallel Mining for High Utility Itemsets Mining by Efficient Data Structure

Mining high utility itemsets in transaction database is an important task in data mining and widely applied in many areas. Recently, many algorithms have been proposed, but most algorithms for identifying high utility itemsets need to generate candidate sets by overestimating their utility and then calculating their exact utility value. Therefore, the number of candidate itemsets is much larger than the actual number of high utility itemsets. In this paper, we introduce the Retail Transaction-Weighted Utility (RTWU) structure and propose two algorithms: EAHUIMiner algorithm and PEAHUI-Miner parallel algorithm. They have been experimented and compared to the two most efficient algorithms: EFIM and FHM. Results show that our algorithm is better with sparse datasets. DOI: 10.32913/rd-ict.vol3.no14.519

Reduced Overestimated Utility and Pruning Candidates using Incremental Mining

Objectives: We proposed a Maximum Utility Growth (MUG) algorithm and Maximum Item Quantity (MIQ) Tree structure is used to decrease the number of candidates and reduce the candidate Itemset. Methods: In high utility Itemset mining profit and quantity of items are important in transaction. Several algorithms have proposed to solve the issues of large number of candidate Itemset generation. The proposed MUG algorithm and MIQ-Tree structure is used to reduce candidate Itemset and overestimated utilities in incremental mining. Moreover, the MUG with second strategy of approach calculates maximum utility of each expanded Itemset from the current prefix with supports in mining process. Finding: MUG algorithm and MIQ-Tree are proposed for mining high utility itemset to reduce the overestimated utility and it proposed two strategies for pruning candidate item sets efficiently in the process. It reduces the number of candidate and improves the performance of incremental mining. MIQ-Tree proposed to construct the tree with single-pass. The tree structure can restricted without new database and it employs decreasing the overestimated utility. MUG proposed to prune the number of candidate itemset with two strategies (I) Pruning 1-Itemset Candidates with Real Item Utilities and (II) Pruning Candidates with Estimated Maximum Itemset Utility. The experimental shows that the method developed the performance by decreasing number of candidate itemset. Performance evaluation shows that it reduces the number of candidate and its runtime with equal usage memory. Through the strategy, it can effectively eliminate the search space in the process. Applications: There is large amount of real world application such as retail marketing and stock marketing has emerged techniques high utility Itemset mining.

Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review

Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multiple scanning databases is main shortage of the Apriori algorithm. Because of the multilevel association execution time is reduced and throughput increase in new methods.MRA algorithm using Bayesian probability, concept hierarchy ,COFI-tree method, dynamic concept hierarchy are used for increase performance of multilevel association rule mining.

Mining Fuzzy Association Rules Based on Parallel Particle Swarm Optimization Algorithm

The association rule extraction process often involves a large number of candidate item sets and multiple read operations on data sets. With the emergence of massive data, the sequential association rule extraction algorithm also suffers from large I/O overhead and insufficient memory. This paper presents a new multi-swarm parallel multi-mutation particle swarm optimization algorithm (MsP-MmPSO) to search several groups in parallel. Experimental results show that the MsP-MmPSO algorithm has an advantage in terms of execution time over traditional particle swarm optimization, especially when the amount or dimensions of the data increase. Experiments also verify that a good task allocation method can reduce the execution time of the parallel algorithm.

English

Now a days high utility item sets specially from large transaction databases is required task to process many day to day operations in quick time. In many relevant algorithms presented those are surface the problem of generating large number of candidate item set and thus degrades the mining performance in terms of execution time and space. In this paper mainly three algorithms are presented, such as UP-Growth (utility pattern) for mining high utility item set for pruning candidate item sets. In these algorithms, compact tree structure (UP-Tree) is used for discovering the useful item-set so that candidate item is generated with only two scan of database. The performance of UP-Growth was evaluated in comparison with the state-of-theart algorithms on different types of datasets. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further. The experimental results show that UP-Growth not only reduces the number of candidates effectively but also outperforms other algorithms substantially in terms of execution time, especially when the database contains lots of long transactions. If we did not use one of the above algorithm then we will face number of problems like if we are retrieve items then that operation takes more time to scan database. It is a time consuming process. This process reduces performance of the project. So we are prefer other alternatives like using UP growth algorithm, FP growth algorithm etc. In these algorithms also have some drawbacks like time consuming i.e. scans database for multiple times. So all aspects we considered and proposed one algorithm in this project i.e. UP Growth algorithm. It is very efficient algorithm for mining high utility compare to FP growth algorithm and etc.

Utility Mining Algorithm for High Utility Item sets from Transactional Databases

The discovery of item sets with high utility like profits is referred by mining high utility item sets from a transactional database. Although in recent years a number of relevant algorithms have been proposed, for high utility item sets the problem of producing a large number of candidate item sets is incurred. The mining performance is degraded by such a large number of candidate item sets in terms of execution time and space requirement. When the database contains lots of long transactions or long high utility item sets the situation may become worse. Internet purchasing and transactions is increased in recent years, mining of high utility item sets especially from the big transactional databases is required task to process many day to day operations in quick time. There are many methods presented for mining the high utility item sets from large transactional datasets are subjected to some serious limitations such as performance of this methods needs to be investigated in low memory based systems for mining high utility itemsets from large transactional datasets and hence needs to address further as well. Another limitation is these proposed methods cannot overcome the screenings as well as overhead of null transactions; hence, performance degrades drastically. During this paper, we are presenting the new approach to overcome these limitations. We presented distributed programming model for mining business-oriented transactional datasets by using an improved Map Reduce framework on Hadoop, which overcomes not only the single processor and main memory-based computing, but also highly scalable in terms of increasing database size. We have used this approach with existing UP-Growth and UP-Growth+ with aim of improving their performances further. In experimental studies we will compare the performances of existing algorithms UP-Growth and UP-Growth+ against the improve UP-Growth and UP-Growth+ with Hadoop.

The Research of PApriori Algrithm Based on OLAP

In analysis of association rules based on OLAP, the application of Apriori algorithm may need to scan the database frequently and generate massive number of candidate itemsets because of the complex system. According to the weakness, the paper proposes PApriori algorithm based on pretreatment, which reduces the time of scanning database to once, while not directly generate candidate itemsets, raise the efficiency Analysis of association rules based on OLAP efficiently.

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.

A Web Data Mining Algorithm Based on Weighted Association Rules

Association rules mining is attracting much attention in research community due to its broad applications. Existing web data mining methods suffer the problems that 1) the large number of candidate itemsets, which are hard to be pruned, should be pruned in advance. 2) the time of scanning the database, which are needed to scan transactional database repeatedly, should be reduced. In this paper, a new association rules mining model is introduced for overcoming above two problems. We develop an efficient algorithm-WARDM(Weighted Association Rules Data Mining) for mining the candidate itemsets. The algorithm discusses the generation of candidate-1 itemset, candidate-2 itemset and candidate-k itemset(k>2),which can avoid missing weighted frequent itemsets. And the transactional database are scanned only once and candidate itemsets are pruned twice, which can reduce the amount of candidate itemsets. Theoretical analysis and experimental results show the space and time complexity is relatively good, Meanwhile the algorithm decreases the number of candidate itemsets, enhances the execution efficiency.

Research and improvement on Apriori algorithm of association rule mining

The classic Apriori algorithm for discovering frequent itemsets scans the database many times and the pattern matching between candidate itemsets and transactions is used repeatedly, so a large number of candidate itemsets were produced, which results in low efficiency of the algorithm. The improved Apriori algorithm improved it from three aspects: firstly, the strategy of the join step and the prune step was improved when candidate frequent (k+1)-itemsets were generated from frequent k-itemsets; secondly, the method of dealing with transaction was improved to reduce the time of pattern matching to be used in the Apriori algorithm; in the end, the method of dealing with database was improved, which lead to only once scanning of the database during the whole course of the algorithm. According to these improvements, an improved algorithm was introduced. The efficiency of Apriori algorithm got improvement both in time and in space. The experimental results of the improved algorithm show that the improved algorithm is more efficient than the original.

Depth-first search algorithm for mining frequent closed itemsets

Mining frequent closed itemsets is a fundamental and important issue in many data mining applications.A new depth-first search algorithm for mining frequent closed itemsets called depth-first search for frequent closed itemsets(DFFCI)was proposed,which could reduce the number of candidate itemsets and the cost of support counting.DFFCI projected the dataset information stored by the improved Compressed Frequent Pattern tree(CFP-Tree)into the partition matrix,and improved the efficiency of support counting by using binary vector logic operation.Global 2-itemset pruning based on support pre-counting and local extension pruning were used to prune the search space effectively.The experimental results show that DFFCI outperforms other depth-first search algorithms.

Approach for mining association rules based on rough set

In this paper a novel approach for mining association rules based on rough set theory was presented.It firstly used the rough set's feature attributes reduction algorithm to reduce attributes,and then applied the improved Apriori algorithm to mine association rules based on the reduction decision table.The advantages of this method were that it reduced the number of the attributes by reducing the unimportant attributes,also reduced the number of candidate item sets and could produce the association rules by scanning decision table only once.The results of application and experiments show that this method is fast and effective.

Granular computing approach to finding association rules in relational database

Granular computing is a new information-processing method. The main objective of this paper is to present a granular computing approach to finding association rules in relational databases. Elementary granules are generated by scanning relational database, and granule table structure for storing information granules is established. By keeping attributes in order and referring to granular computing, frequent k-item sets are gradually generated from frequent 1-item sets, frequent 2-item sets, and so on. Corresponding algorithms are proposed and illustrated with a real example. Experiments on real data show that the algorithms can reduce the number of candidate item sets and save the computing time. © 2009 Wiley Periodicals, Inc.

INDEX-MAXMINER: A NEW MAXIMAL FREQUENT ITEMSET MINING ALGORITHM

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative to address the problem. Set-Enumeration Tree (SET) is a common data structure used in several MFI mining algorithms. For this kind of algorithm, the process of mining MFI's can also be viewed as the process of searching in set-enumeration tree. To reduce the search space, in this paper, a new algorithm, Index-MaxMiner, for mining MFI is proposed by employing a hybrid search strategy blending breadth-first and depth-first. Firstly, the index array is proposed, and based on bitmap, an algorithm for computing index array is presented. By adding subsume index to frequent items, Index-MaxMiner discovers the candidate MFI's using breadth-first search at one time, which avoids first-level nodes that would not participate in the answer set and reduces drastically the number of candidate itemsets. Then, for candidate MFI's, depth-first search strategy is used to generate all MFI's. Thus, the jumping search in SET is implemented, and the search space is reduced greatly. The experimental results show that the proposed algorithm is efficient especially for dense datasets.