Algorithm for discovering frequent item sets based on optimized and regrouped item sets

In order to solve these problems how to easily generate candidate frequent item sets and fast compute support of candidate item sets, an algorithm of association rules mining based on binary has been introduced. However, one presented binary mining algorithm is only suitable for mining some relative short frequent item sets since the way of generating candidate item sets is also similar to Apriori, another is only suitable for mining long frequent item sets, which generates candidate item sets by up-down search strategy. And so aiming to mining general frequent item sets, this paper proposes an algorithm of association rules double search mining based on binary, which is different from tradition association rules mining algorithm based on double search strategy. The algorithm doesn’t use combination of set theory to generate candidate item sets but binary logic operation that is also used to compute support of candidate item sets, which can use character digital to reduce the number of scanned transaction. The algorithm gets rid of shortage about some presented algorithms based on binary. The experiment based on above three algorithms indicates that the efficiency about double search strategy is fast and efficient when mining general frequent item sets which aren’t confined.


 
 
 
 Apriori algorithm is a classical algorithm of association rule mining and widely used for generating frequent item sets. However, the original Apriori algorithm has some limitation such as it needs to scan the dataset many times to discover all frequent itemset and generate huge number of candidate itemset. To overcome these limitations, researchers have made a lot of improvements to the Apriori such as candidate generation, without candidate generation, transaction reduction, partitioning, and sampling. When it comes to mine massive data, these algorithms failed to prove efficiency because limitation of the processing capacity, storage capacity, and main memory constraints. Therefore, parallel and distributed algorithms are developed to perform large-scale computing in ARM on multiple processors. However, the problems with most of the parallel and distributed framework are overheads of managing distributed system, lack of high level parallel programming language, and node failures. Hadoop-MapReduce is an efficient, scalable, and simplified programming model for massive data processing and it also available on cloud environment. Cloud computing offers huge computing resources, and capacities to solve big data challenges. Recently many parallel algorithms have been proposed on Hadoop-MapReduce to enhance the performance of Apriori algorithm but there are some drawbacks: since multiple scan over the dataset is needed to generate candidate itemset, it consume more execution time. The aim of this study is to propose a parallel Transaction Reduction MapReduce Apriori algorithm (TRMR-Apriori) which is reduce unnecessary transaction values and transactions from the dataset in parallel manner to overcome above problems. The experiments show that TRMR-Apriori is able to achieve better execution time to discover frequent itemset those of previous sequential ARM algorithms such as Apriori, AprioriTid, Eclat, and FP-Growth and the previous parallel algorithms such as PApriori, MRApriori, and Modified Apriori with different condition on homogeneous computing environment using Hadoop-MapReduce platform in cloud. Overall, the TRMR-Apriori shows the strength to extract the frequent itemset from massive dataset in cloud.
 
 
 
 

Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms existed "item sets generation bottleneck" problem, and are very time-consuming. An enhance algorithm associating which is based on the user interest and the importance of itemsets is put forward by the paper, incorporate item that user is interested in into the itemsets as a seed item, then scan the database, incorporate all other items which are in the same transaction into itemsets, Construct user interest itemsets, reduce unnecessary itemsets; through the design of the support functions algorithm not only considered the frequency of itemsets, but also consider different importance between different itemsets. The new algorithm reduces the storage space, improves the efficiency and accuracy of the algorithm.

The association rule mining algorithm Apriori need to repeatedly scan the transaction database and a lot of I/O loads, moreover it may generate huge candidate sets, the complexity of time and space is relatively high. Aiming at the limitation of the algorithm, an algorithm is proposed for association rule mining based on matching array. The algorithm only needs to scan the database once, screens out 1-frequent item sets and encode them. The latter process of the algorithm is candidate encoding operation and the effectiveness verification of frequent item sets, without scanning the database, and does not process the invalid frequent items for matching array. The experimental results under the same condition show that the improved algorithm can reduce the times of scanning database and the number of candidate by deleting candidate, thereby improving efficiency of the algorithm.

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.

Association rules mining is one of the most important problems in knowledge discovery and data mining. The goal of it is to acquire consumption habits of customers by discovering the relationships between items from a transaction database that has a large number of transactions and items. The most compute intensive process for ARM is to find out the frequent 1-itemsets and 2-itemsets. In this paper, we propose a quantum algorithm for finding out the frequent 1-itemsets and 2-itemsets. In our algorithm, to mine the frequent 1-itemsets efficiently, we use the technique of amplitude amplification. To mine the frequent 2-itemsets efficiently, we propose a new tomography scheme, i.e., pure-state-based quantum state tomography. It is shown that our algorithm is potential to achieve exponential speedup in the number of transactions and polynomial speedup in the number of items over the classical algorithm.

Association rule mining (ARM) is well-known issue in data mining. It is a combinatorial optimization problem purpose to extract the correlations between items in sizable data-sets. According to the literature study, bio-inspired prove their efficiency in term of time, memory and quality of generated rules. This paper investigates multi-population cooperative version of bat algorithm for association rule mining (BAT-ARM) named MPB-ARM which is based on bat inspired algorithm. The advantage of bat algorithm is the power combination between population-based algorithm and the local search, however, it more powerful in local search. The main factor to judge optimization algorithms is ensuring the interaction between global diverse exploration and local intensive exploitation. To maintain the diversity of bats, in our proposed approach, we introduce a cooperative master-slave strategy between the subpopulations. The experimental results shows that our proposal outperforms other bio-inspired algorithms already exist and cited in the literature including our previous work BAT-ARM.

Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.

In the process of modern graphic design, designers usually get inspiration from a large number of art graphs and then choose graphic elements for graphic design. However, the process is generally manual, limiting the alternatives a designer can explore. In this paper, we propose GDAssister, a graphic design assistant system with optimal algorithm of associated rule mining. The system can automatically generate corresponding graphs according to the requirements of designers and use style transfer technology to change the style (color and texture) of the graphs. The system introduces four modules referring to the process of graphic design (e.g., graphic element extraction, association rule mining, automatic layout, style transform). Association rule mining is the core module of the system, and the efficiency of association rule mining algorithm determines the running efficiency of GDAssister. We design an improved association rule mining algorithm (IFP-Growth), which can reduce time consumption and memory usage. We use Chinese ethnic apparel database to verify the feasibility and efficiency of the system. Our experiment shows that GDAssister: (1)helps designers create more graphic alternatives to explore design creativity and (2)helps designers generate different styles of graphs more quickly to shorten the design cycle.

To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic Algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding and the fitness function. Through using probability vector possessing learning properties to update the population, the algorithm increases the convergence speed and enhances the searching ability, compared to GA. In the experiment of mining association, rules in blood indices data, PBIL algorithm performs better not only in running time, convergence speed, but also achieve better searching results. Meanwhile, this paper proposed a parallel algorithm for association rule mining based on PBIL and designed a system architecture based on cloud computing for blood indices analysis, providing a good example to apply the new algorithm to cloud computing.

A novel software defect prediction approach via weighted classification based on association rule mining

In this paper, an application method of association rule mining algorithm based on 5G technology in information management system is proposed to solve the problems of long running time and low processing efficiency in traditional financial information processing system. The association rule mining algorithm's employment in information management systems is the main topic of this research study, which is based on 5G technology. The efficiency and efficacy of information management systems have a lot of room to grow with the introduction of 5G. Large datasets may be mined for patterns and associations using the potent approach known as association rule mining. We want to improve the performance of information management systems by fusing association rule mining with the capabilities of 5G technology. The experimental findings indicate that in the first group of trials, the traditional system’s time for information mining is identical to that of the developed system, which is around one minute. The typical system's time to mine financial information, however, steadily grows with the amount of experimental data. The difference between the two is most obvious in the sixth experiment. Because the design system can delve deeply into the financial information, the overall information mining time of the financial information management system based on the association rule mining algorithm of the design is shorter. It is confirmed that the system for automatically processing financial information described in this study has a high level of processing accuracy and a positive processing outcome.

Big data has become one of the key sources for valuable information and as information becomes larger it poses some computational challenge in finding a best possible solution for mining association rules and discovering patterns in data. Meta-heuristic algorithm when applied to mining association rules aims to find best possible rules from data without being stuck in local optimal. Example of meta-heuristics algorithm includes genetic algorithm and particle swarm optimization algorithm. Finding appropriate representation of various types of patterns using rough numerical values attributes is still a challenge because most association rules cannot be applied to numerical data without discretization which may lead to information loss. Mining numeric association rules is a hard optimization problem rather than being a discretization, thus, this paper proposes a new meta-heuristic algorithm which uses wolf search algorithm (WSA) for numeric association rule mining from rough values within tolerable ranges.

DATA mining is a vital analysis domain today that focuses on data discovery in databases. It’s wherever knowledge from the database are mined in order that informative knowledge will be made and used effectively and with efficiency by humans. Prediction and description are its objectives. One amongst the aspects of information mining is that the Association Rule mining. It consists of two procedures: First, finding the frequent item set within the info employing a minimum support and constructing the association rule from the frequent item set with such confidence. It relates to the association of things whereby for each prevalence of A, there exists an occasion of B. This mining is additional applicable within the market basket analysis. Purchasers who buy certain items will benefit from that application. What is the attainable item that matches every item that they purchase? Among the most widely used association rule mining algorithms are MLPs and genetic algorithms.

Because of the rapid growth in worldwide information, efficiency of association rules mining (ARM) has been concerned for several years. In this paper, based on the original Apriori algorithm, an improved algorithm IAA is proposed. IAA adopts a new count-based method to prune candidate itemsets and uses generation record to reduce total data scan amount. Experiments demonstrate that our algorithm outperforms the original Apriori and some other existing ARM methods.